@article{ author = {R.Shurouni, and M.R.Malek,}, title = {Time-Location-preference aware Recommender system in LBSN}, abstract ={The rapid growth of location-based social networks through attracting millions users, reveal the high popularity in the short time period. A result of the location based social network services, access to a large collection of data that can extract the spatial history, social relationships structure, movement behavior and characteristics of users. Social, spatial and temporal data analysis leads to create a wide range of location based services. By using statistical and knowledge discovery techniques for advising unvisited locations to users and decreasing large data volume problems, recommendation systems have become the popular services of these networks. Recommendation system is an approach to deal with the problems caused by large amount and growing volume of information and helps user to approach his/her goal among the enormous amount of information. In this paper, we design the novel GEO-FIF method to recommend unvisited places to tourists based on their location history. This time, location and preference aware recommendation system, offer a set of locations near to the user's current position with regard to the time term and the geographical distance between end user and other users as well user preferences that is extracted from visited locations. In the first stage, we survey the impact of the distance between users on common visited locations. Then we measure users’ interest into the locations by creating a user – location matrix within the period of one day and utilizing the content based filtering that for each user calculates a score for each place. In the next step by having the current position of the user, collection of places is limited according to their distance from the user. This is in addition to speed up processing and computing, increase recommendation accuracy. Finally, by using an innovative function, we estimate the similarity of the target user and other users based on the combination of the distance between them and the given score to places by other users. The score of a place for the target user is calculated by accumulative scores given by other users. In fact, we utilize collaborative filtering method to measure the similarity between users and predict the user interest to a new location based on accumulative scores of similar users. Finally combining these methods provides k unvisited locations within specific distance to user’s real-time location and its current time period. In this paper, Gowalla check-in data for Beijing, China were used in the period between October 2011 and November 2011. To evaluate the performance of the proposed method, the results of this method were compared with the results of two basic recommendation system methods in terms of rank accuracy indicator which is most common method for assessing recommender systems. The proposed method increases precision 15 and 12 percent in compared to user-based collaborative filtering using binary methods and GM-FCF, respectively.}, Keywords = {Location based social network,Location History,Recommender system}, volume = {5}, Number = {1}, pages = {1-12}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-222-en.html}, eprint = {http://jgst.issgeac.ir/article-1-222-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Aslani, and M.Taleai,}, title = {Using Soft Computing in Geospatial Information Systems for Spatial Modeling (Case study: Mineral Potential Mapping)}, abstract ={In GIS, data is organized in themes as spatial layers. One of the most significant tasks of GIS is to analysis, spatial layers in order to model spatial phenomena. Since spatial data in GIS are inherently uncertain, a system that handles and infers from such uncertain data is of vital importance. Insufficient consideration to spatial modeling can lead to several problems in spatial decision making, death toll, property damage, financial loss and other hardships. There is no analytical solution in most spatial modeling. For these modeling, methods inspired by nature sometimes work very efficiently and effectively. These biologically inspired methods are called Soft Computing. The modeling of mineral potential is one of the special cases of the problem of spatial modeling in which no comprehensive model has been developed for it. A mineral potential mapping which depicts the favorability of mineralization occurring over a specified area is an important process for mineral deposit exploration. The purpose of this paper is to suggest several soft computing methods such as a fuzzy inference system (FIS), neural networks and genetic algorithms in a GIS framework for mineral potential mapping. A typical FIS is composed of two main parts: The Knowledge Base (KB) and the Inference System. The KB composed of Data Base (DB) and Rule Base (RB) stores the available knowledge about in the form of linguistic “IF-THEN” rules. One of the major problems in constructing an FIS is to build the knowledge base. In the conventional design methods, the desired rules and functions are based on the expert's knowledge and experiences. However, we cannot perfectly represent the expert's knowledge by linguistic rules nor choose appropriate membership functions for fuzzy sets. Moreover, converting the experts' knowledge into if-then rules is difficult and often results are incomplete, unnecessary and include conflicting knowledge, since experts cannot express all their knowledge. These problems can be sorted out applying techniques to construct a fuzzy knowledge base of numerical input-output data. In this research, two methods of dividing the input-output spaces and neural network are implemented to deal with this problem. To evaluate the constructed FIS, the genetic neural network has been applied and its results compared to the results of the FIS. In a genetic neural network, genetic algorithm is used for neural networks in order to optimize the network architecture. As a matter of fact, the topology of the networks is encoded as a chromosome and some genetic operators are applied to find an architecture which fits best the specified task according to some explicit design criteria. Numerical experimentations showed that the genetic neural network used in this study is the most successful method. It could predict the characteristic of 85% boreholes correctly. The results also indicated that genetic neural network and FIS with RMSE of 4 and 15 respectively are more accurate methods.}, Keywords = {Geospatial Information System, Fuzzy Inference System, Genetic Algorithm, Neural Networks, Mineral Potential Mapping}, volume = {5}, Number = {1}, pages = {13-24}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-91-en.html}, eprint = {http://jgst.issgeac.ir/article-1-91-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {S.Abolhasani, and M.Karimi, and M.Taleai,}, title = {Modeling Index of land use Suitability Based on Vector-Based Cellular Automata by Focusing on Neighborhood Effect and Application to Simulating Land Use Changes}, abstract ={Introduction: In the recent decades, raster based cellular automata (CA) models have been used increasingly in simulating land-use changes and urban growth because of simplicity of the computation and their conformity with remote sensing data. Current researches have introduced vector based cellular automata to tackle with some limitations that raster based conventional cellular automata models are faced with, such as sensitivity to spatial scale. The purpose of this study is modelling land use suitability to simulating land use changes through vector based cellular automata with application of cadastral parcels instead of pixel. Materials and Methods: In the proposed model, urban land use suitability is evaluated based on physical suitability, accessibility and neighborhood effect. Two parameters of altitude and slope are combined together by weighted linear combination (WLC) method and are representative physical suitability and Euclidean distance of each parcel from the closest point of path network is utilized to measure accessibility factor. Spatial externalities of neighborhood land uses are important component in simulating land use changes and using cellular automata is justifiable if neighborhood rules, as heart of the cellular automata models, are defined correctly. One of the limitations of neighborhood rules is the lack of theoretical foundation and empirical validation. So, CA urban models are technology driven rather than theory, due to considering hypothetical ideas about urban dynamics. Therefore, neighborhood rules are often defined by trial and error. In this study, the neighborhood effect is defined based on a function of three components including compactness, dependency and compatibility and the way of land use interactions is studied in three service levels of local, district and regional. Compactness is the tendency toward creating a kind of land-use adjacent to another similar one dependency is depending one land-use on the other land-uses for supplying its needs and compatibility is defined as the arrangement of two or more land-uses together without any significant negative effects. The neighborhood effect of each parcel for each land-use is calculated from total effects of all adjacent parcels in the form of three classes of compactness, dependency and compatibility. It should be considered that each of factors has various impacts in land-use interactions and can be combined together with different weights, regarding the subject land-use. Therefore, relative importance of three factors is defined using expert knowledge captured based on AHP method and then the neighborhood effect of each parcel is computed using to WLC method. Results and Conclusions: This model is implemented and assessed using data of Tehran 22 municipal region, in two scenarios: land use preference and maximum suitability. The results are illustrated high ability of the model in simulating land use interactions and presenting urban growth in an acceptable level of consistency among the urban land uses. Evaluating the results in the case study area shows an average score 0.77 and 0.55 in Scenario 1 and 0.75 and 0.47 in scenario 2 (from 0 to 1) for compatibility and dependency of new allocated parcels, respectively. The proposed model can be utilized by urban planners to assess different urban scenarios for better decision making and urban planning.}, Keywords = {Land use suitability, neighborhood effect, cadastral parcels, vector-based cellular automata, GIS}, volume = {5}, Number = {1}, pages = {25-42}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-61-en.html}, eprint = {http://jgst.issgeac.ir/article-1-61-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {S.Honarparvar, and A.A.Alesheikh,}, title = {Development of automatic updating spatial database by Volunteered Geographic Information}, abstract ={Modern cities require an optimum management of various spatial elements such as electrical distribution network, road network, urban servicing centers, and public facilities. Spatial information usually is stored in a geodatabase for a better data accessing, removing, adding, and transferring. Capital cities, generally, are experiencing many changes with respect to construction and land use. Tourism services quality, location and characteristics may change in such cities too. An example of such changes can be observed in restaurants.  Thus, updating such geodatabases is very necessary, to gain tourists trusts in using a tourism application services (e.g. an application about finding desire restaurants). Traditional or authorized data acquisition’s methods consume huge amount of time and money, while Volunteered Geographic Information (VGI) are almost free of such limitations. Therefore, VGI has been considered as an alternative solution for some applications which do not require much money or have to be updated during a real time process. VGI has also been widely used when application developers expect automatic performance of a system. The aim of this paper is to put the capability of automatic updating to a geodatabase of restaurants information by Volunteered Geographic Information in practice. To do so, a mobile application has been built to collect volunteers’ data, but all input information should be confirmed for data qualification. Thus, the application should filter VGI data based on their quality. Therefore, VGI quality should be assessed first, and then the data with acceptable quality should be chosen and used in geodatabases. VGI quality inherits spatial data quality characteristics. Hence, it is multidimensional and includes various elements (e.g. positional accuracy, thematic accuracy, temporal accuracy, completeness, logical consistency and etc.) which are different in every application. In this paper positional accuracy, temporal accuracy, thematic accuracy and credibility are considered as effective elements of VGI quality. These elements should be integrated, because the geodatabase require an explicit parameter so as to decide which data deserves to join others. In such cases, Multi Criteria Decision Making (MCDM) methods can be useful for deciding how to integrate criteria to get realistic results. As experts from various fields evaluate the quality of elements, recognizing most important criteria is hard and it is arduous to specify weighs for each element. So, it is required to employ a method that decreases the effect of weighting criteria in MCDM operation. In this paper, among MCDM methods, Ordered Weighted Averaging fuzzy quantifier guided operator is selected to merge VGI quality elements and assess final quality. The proposed method tests various scenarios and help decision makers to decrease weighting effect. After testing the mobile application, final results demonstrated that the application is successful in completing information of 60% of restaurants. As a conclusion of the research, it is cleared that high thematic VGI quality will prevent errors in populated restaurants area.}, Keywords = {Volunteered Geographic Information, automatically updating geo-database, OWA and fuzzy quantifiers, VGI data quality, restaurants, mobile application}, volume = {5}, Number = {1}, pages = {43-53}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-261-en.html}, eprint = {http://jgst.issgeac.ir/article-1-261-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.MalekNejad, and H.Ghassemian, and F.Mirzapour,}, title = {GLCM Texture Features Efficiency Assessment of Pansharpened Hyperspectral Image Classification for Residential and Industrial Regions in Southern Tehran}, abstract ={Most of common classification algorithms in remote sensing are based on spectral characteristics of pixels. These approaches ignore the spatial information of data, such as texture, in classification process. Simultaneous usage of texture and spectral information is a new trend in remote sensing image classification which has been considered in this study. We have evaluated the efficiency of gray-level co-occurrence matrix texture features (GLCM) extracted from panchromatic (PAN) image of ALI detector in improving the classification accuracy of Hyperion hyperspectral (HS) data in urban regions of Tehran. Classification is performed using a support vector machine (SVM) classifier with a Gaussian kernel. In our experiments, we have considered three different cases: a) classifying original Hyperion data, b) classifying Hyperion data pansharpened by color normalized transform (CNT), and finally, c) simultaneous use of GLCM texture features of panchromatic data and the spectral features of pansharpened data in classification process. In case b and c, for pansharpening HS data we have performed the following steps: Registering HS data with PAN data using nineteen ground control points, polynomial warping of second order, and Nearest neighbor interpolation. Selecting a subset of HS bands which spectrally overlap with PAN image. Fusing the spatial information of PAN image into the HS subset bands, obtained in step 2, using CNT method. Moreover, as GLCM features, we have extracted 8 texture features from GLCM matrices: mean, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment, and correlation. In order to assess the influence of the size of GLCM extraction window on the quality of texture features, we have considered various window sizes: 3×3, 5×5, 7×7, and 9×9. At the first phase of our experiments, we compared classification results obtained using the original HS data with the results obtained from the pansharpened HS subset – see Table 1. The results showed an increase of about 15% in the average classification accuracy when using the pansharpened data. At the second phase, we combined each of the texture features individually with the pansharpened HS subset. The results are given in Table 2. As the table suggests, regardless of the type of texture feature and the size of the GLCM extraction window, the combinations improve the overall classification accuracy (OA) of data. However, texture features show better quality when extracted from GLCM matrices obtained using 9×9 neighborhood windows. In addition, we observe that regardless of the size of GLCM window, dissimilarity feature delivers the best results. To summarize, by using the pansharpened HS subset instead of the original HS data, we achieved about 15% gain in the classification accuracy. Moreover, combining dissimilarity texture features –extracted from GLCM matrices obtained using 9×9 neighborhood windows– with the pansharpened HS subset improved classification results. In our experiments, we achieved about 5% increase in OA compared to that of using pansharpened HS subset alone. Table 1. Comparison of classification accuracies obtained using original HS data with those obtained from pansharpened HS subset. Class number Original HS data Pansharpened HS subset Increase in accuracy 1 91.4 99.7 8.3 2 96.3 97.0 0.7 3 99.1 99.6 0.6 4 61.9 70.1 8.2 5 39.4 40.6 1.2 6 69.2 45.8 -23.4 7 1.9 0.6 -1.3 8 98.1 98.1 0 9 67.3 71.3 4.0 10 21.4 76.9 55.5 11 0.0 87.2 87.2 12 99.4 99.2 -0.2 13 99.1 95.4 -3.7 14 3.7 73.2 69.5 Average 60.6 75.3 14.7 Table 2. Comparison of overall classification accuracies (OA) of “pansharpened HS subset” with those of “pansharpened HS subset + GLCM texture feature*”. *GLCM texture feature (combined with pansharpened HS subset) GLCM extraction window size 9×9 7×7 5×5 3×3 Mean 90.1 88.5 87.3 86.6 Variance 87.6 86.6 86.6 86.3 Homogeneity 89.7 88.7 88.7 86.9 Contrast 86.8 86.6 86.4 86.4 Dissimilarity 90.5 90.2 90.0 88.3 Entropy 90.5 89.0 88.9 87.1 Angular second moment 90.1 88.5 88.6 87.3 Correlation 87.3 87.4 88.0 87.3 Pansharpened HS subset (no GLCM features) 86.1}, Keywords = {Remote sensing, Hyperspectral imagery, Image fusion, Image texture, Classification}, volume = {5}, Number = {1}, pages = {55-64}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-390-en.html}, eprint = {http://jgst.issgeac.ir/article-1-390-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {H.Yarmand, and M.Mokhtarzedeh, and A.Mohammadzadeh, and M.J.ValadanZoej,}, title = {A Novel Method for Accurate Extraction of Candidate Road Intersection Points from High Resolution Satellite Images}, abstract ={The increasing number of satellite sensors, with various characteristics in terms of spatial and spectral resolution, motivates the developments of automatic methods for better exploitation of their huge available information content. In this regards high resolution satellite images, which provide very detailed and accurate information of the urban areas, have attracted more research attentions. As an early step, geo-referencing of satellite images are essential by which image position observations can be transformed to the ground position information. This process needs some accurate control points which are traditionally provided by human operators. Considering the time-consuming and tedious task of control point provision automatic feature extraction from satellite images, especially distinct point features, has been the subject of many researches in the last decades. In this paper a novel method is proposed for accurate automatic extraction of road intersection points in candidate regions. These point features, if accurately extracted, are applicable for image geo-referencing or image-to-image registration. Roads are presented as wide ribbons in high resolution satellite images which cause a considerable uncertainty about the exact position of their intersection points. This uncertainty, which makes road intersection extraction a difficult task for human operators, motivates the aim of this research to develop an automatic and accurate extraction method. In the first step of the proposed method the candidate intersection region is divided to three clusters via a simple K-means clustering. The largest cluster is than labeled to background and omitted from the rest of processing. In continue angular texture information, extracted from a rectangular element, is used to detect road pixels. Road edge pixels are then found via the famous canny edge detector. Among the detected road edge pixels those which are close to the junction are found using Circle Centered Edge analysis. In the last step, a novel vectorization scheme is proposed to find out the exact position of road junctions. This scheme, developed based on the proximity and parallelism concepts, includes the following sub-steps: 1) line fitting to edge pixels here a line is fitted to the fist ten pixel and then the inclusion hypothesis of other pixels is verified by the evaluation of their distance to the fitted line, 2) eliminating the lines which are not related to road and determination of parallel lines, 3) grouping and merging of the same-type lines, 4) determining the co-side lines (i.e. road lines which are at the same side of a road segment) and 5) road centerline determination followed by positioning of the road intersection point. Given the road junction area, where at least 3 road segments intersect, the focus of this research is only on the exact and automatic positioning of the intersection point. The proposed method was implemented on four IKONOS sub-images taken from an urban area, Shiraz Iran, where in all cases the 0.5 accuracy proved the efficiency of the method. This method is also rotation invariant which means its applicability for all road segment orientations. Being resistant against the presence of vehicles and also the capability to extract more than one intersection points in a candidate region are other advantages of the proposed method.}, Keywords = {Road Network Intersection, K-means, Angular texture, Circle centered around the junction, Line fitting}, volume = {5}, Number = {1}, pages = {65-74}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-391-en.html}, eprint = {http://jgst.issgeac.ir/article-1-391-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {P.Pahlavani, and H.AminiAmirkolaee,}, title = {3D reconstruction of buildings with flat roofs using LiDAR data and digital aerial images}, abstract ={In this paper, an approach has been proposed in order to reconstruct the flat buildings using the LiDAR data and the digital aerial images because on one hand, these types of buildings constitute the main structure of IRAN mega cities. On the other hand, separating the roof planes and reconstructing them is a challenging matter due to the fact that the normal vectors of the building roof planes are completely the same and there is not any intersection among them. In this regard, firstly, 16 potentially primary features were produced and the optimum features were extracted using the genetic algorithm and the KNN algorithm to detect the buildings. Subsequently, an approach was presented to eliminate the misclassified regions and to improve the detection results. In the designed reconstruction approach, each building parcel was considered separately in order to reduce data redundancy and increase the result accuracy. After selecting the considered parcel, an initial class for building roof planes was achieved by employing the surface slope differential. Actually, a threshold was specified and the regions with the slope differential value less than it were removed. Therefore, an initial class of primary planes were recognized and labeled by connected component algorithm. The roof planes were improved and detected completely by textural and altitudinal analyzing. The acceptable range was determined by computing the median minus the variance of the elevation in the plane to the median plus the variance. The median was selected as a criterion, because it is not sensitive to the noise of data and it causes to choose a reliable value. To identify the adjacent planes, the recognized plane was scanned row by row and column by column. In each row/column, the pixels with values more than zeros were extracted and analyzed. If there exists a variation, the pixels numbers were extracted and considered as the adjacent. Afterwards, the boundary nodes of each plane were extracted using the chain code algorithm. The optimum nodes should be selected as boundary and the planes should be placed beside each other without any intersection and gap. Hence, by investigating the distance and angle, very close nodes were removed and replaced by their mean. Also, the nodes that cause creating the intersection or gap were recognized and rectified in order to eliminate this error. Then by searching around of the extracted nodes, the corresponding terrain node for each boundary node was obtained. The equation of each plane was computed by the coordinates of the inner nodes in that plane. Finally, the equation of planes, extracted boundaries, and nodes of the floor were utilized for reconstructing the final model. The proposed approach was implemented on some building blocks with different structures and the accuracy of the reconstructed plane was evaluated in both altimetric and planimetric criteria. The evaluated results were shown 84.56% accuracy on average for planimetric reconstruction, 0.212 meter root mean squared error for planimetric corner coordinate, and 0.145 meter root mean squared error for altimetric reconstruction. These results clarify the good performance of the proposed approach for reconstructing buildings with flat roofs.}, Keywords = {Detection, reconstruction, feature, flat roof, LiDAR, digital aerial image}, volume = {5}, Number = {1}, pages = {75-92}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-220-en.html}, eprint = {http://jgst.issgeac.ir/article-1-220-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Jafari, and M.J.Valadanzoej, and Y.Maghsoudi,}, title = {Knowledge-based Classification of Polarimetric SAR data using Support Vector Machine-Decision Tree (SVM-DT)}, abstract ={A number of classification algorithms have been proposed for PolSAR data. There are basically three approaches: (1) algorithms based on a statistical model, (2) algorithms based on the scattering mechanism of electromagnetic waves, and (3) knowledge-based algorithms. In the first category, classification is done based on the specified probability density function. The second approaches, the classification of PolSAR images are based on some form of target decomposition theories. Third approaches included two steps. First, extraction of knowledge from PolSAR data, and second application of this knowledge to the classification of other pixels. In these approaches it is possible to include scattering model results and common knowledge about the targets. The main purpose of the proposed method is the knowledge-based and object-based classification of PolSAR data. To improve the classification results, the contextual information should be considered for incorporation into the classifiers. For this, the object-oriented package eCognition was used to implement the object-oriented image analysis of PolSAR images. The multi-resolution segmentation module was used to perform object delineation in this study. Proposed method can apply prior knowledge, expert knowledge and data knowledge in the process of classification. A combination of Support Vector Machine and Decision Tree (SVM-DT) was presented for fusion of three level knowledge. The SVM based binary decision tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVM-DT architecture was designed to provide superior multi-class classification performance. Utilizing this architecture, N-1 SVMs needed to be trained for an N class problem, this can lead to a dramatic improvement in recognition speed when addressing problems with big number of classes. Since DTs are often constructed using a portion of the training patterns to accomplish individual classifications at each node, the node classifiers should be robust in the presence of “bad” samples or outliers. The SVM’s remarkable performance with regard to sparse and noisy data makes them suitable for binary classification trees. The incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. In this paper the prior knowledge was used for compensating the unbalanced data in SVM classification. Furthermore, the expert knowledge provides information for designing a decision tree and a feature selection for classification algorithm. Also, the data knowledge was used in various classification steps e.g. features for SVM classification and feature selection. A Radarsat-2 image of the Petawawa forest area including six land cover classes: red oak (Or), white pine (Pw), black spruce (Sb), urban (Ur), water (Wa), and ground vegetation (GV) was chosen for this study. In this research, six experiments were provided for evaluating three level knowledge effect: Wishart, SVM, SVM-DT, object-based SVM-DT, object-based SVM-DT and feature selection, adding prior knowledge to object-based SVM-DT and feature selection. The results show the positive effect to forest classes for adding various knowledge to the classification. Although, the effect of some knowledge on other classes is non-positive. Eventually, the overall accuracy of the proposed method is 87.36. The proposed algorithm outperformed the Wishart classifier by 15% and SVM classifier by 9%.}, Keywords = {Polarimetric SAR data, Knowledge-based classification, SVM-DT, Expert knowledge, Forest}, volume = {5}, Number = {1}, pages = {93-108}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-206-en.html}, eprint = {http://jgst.issgeac.ir/article-1-206-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {P.Pahlavani, and S.TalebiNahr, and R.Karimi,}, title = {Building detection using aerial images and LiDAR data via adaptive neuro-fuzzy systems}, abstract ={As adaptive neuro-fuzzy inference system (ANFIS) has shown a high capability in solving various complicated problems, its usage has been increased so far. LiDAR is an active sensor which operates based on measuring distance by sending pulses to the ground and receiving the backscatters. This technology gives the 3D position of a point directly. Because of using millimeter level laser ranging accuracy, LiDAR is highly accurate. Dense point clouds of LiDAR can be directly used in simple applications, but the full manipulation of the LiDAR potentials and capabilities needs new methods and researches that differ from those in traditional Photogrammetry. The main output data of LiDAR are point clouds. Each point has two range and two intensity values for both the first and the last pulses. In some areas where there are some trees, the values for the first and the last pulses may differ, in which the first pulse data includes upper surfaces of trees, whereas the last pulse data includes lower surfaces, mainly ground. ANFIS is able to deal with large amounts of data with linear or nonlinear relations. In our study, the combination of digital aerial images and LiDAR data were used for the first time to probe the capabilities of the ANFIS as a classifier. The fact of non-linearity and ambiguity of this combination makes this challenge so hard. The main goal of this research is to detect buildings in city scenes from digital aerial images and LiDAR data using the ANFIS. In this regard, a genetic algorithm is run for feature selection. Four features were selected by genetic algorithm. These features were generated as ANFIS inputs including Green band, normalized difference vegetation index (NDVI), and normalized digital surface model (nDSM) using two different algorithms via morphological operations. The proposed ANFIS used three different algorithms to build its fuzzy inference system structure including grid partition, subtractive clustering, and fuzzy c-means clustering. Also, as there are many methods in building and tree detection as mentioned before, the main question is which of them is better among the others? This is not an easy question to answer because these methods are not evaluated over a unique data-set. To overcome this problem, fourth working group of third commission (WG III/4) in the international society of photogrammetry and remote sensing (ISPRS) has provided some benchmark data-sets, and has encouraged all researchers around the world to evaluate their methods on these data-sets. The results were evaluated on three different test areas, known as Areas 1, 2, and 3. The achieved results were compared with each other, as well as with ISPRS WG III/4 participants’ results, by considering Completeness, Correctness, Quality, and RMS indices per-area and per-object levels. The achieved results demonstrated the capability of the proposed ANFIS in detecting buildings in complex city scenes in comparison with other methods. Although there are some typical errors among participants’ results, most of these errors have resolved in ANFIS-base approaches. Proposed ANFIS-based methods achieved Completeness of 100% in all three test areas for buildings larger than 50 m2.}, Keywords = {ANFIS, LiDAR, nDSM, Building Detection, Aerial Images}, volume = {5}, Number = {1}, pages = {109-125}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-191-en.html}, eprint = {http://jgst.issgeac.ir/article-1-191-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {R.Attarzadeh, and J.Amini,}, title = {SVM Classifier Optimization using Genetic Algorithm for Classification of Polarimetric Synthetic Aperture Radar Imagery}, abstract ={Satellite image classification is considered as one of the most common approach for information extraction from remotely sensed data. With the advent of microwave sensors and taking into account the advantage of distinctive characteristics of the microwave range in electromagnetic spectrum extraction of different information in comparison to optical sensors are provided. Polarimetric information has significant implications for identifying different phenomena and distinguishing between them. In synthetic aperture radar imagery unlike hyperspectral imagery, where in spectral bands provide required features for pattern recognition process, we need to construct such features. Nowadays we can extract a wide range of features from polarimetric images using target decomposition theorem and SAR descriptors. In this paper at the first stage we try to extract features in three categories including original data features, decomposition features and SAR parameters. Then SVM algorithm with RBF kernel is used to classify polarimetric image. Due to the binary nature of support vector machines algorithm, the one against all approach is used to perform a multi-class classification. In this approach for m class m binary classifier are considered. In this study the genetic algorithm is used in order to calculate kernel parameters, feature space dimension reduction and selection of optimal features. In this study the superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset on Polarimetric imagery are demonstrated. The other point of the paper is higher accuracy of SVM classifier by kernel parameter selection using genetic algorithm and considering all the features in relation to optimal feature selection using genetic algorithm and kernel parameter selection using grid search. In another section of this study object based image analysis is used to compare the performance of SVM classifier in conjunction with genetic algorithm with OBIA. In OBIA approach, at the first stage, an image to be analyzed is segmented into individual image objects in an object based approach. The image pixels from the image are grouped to form the objects in a segmentation process. The created image objects should represent the objects in reality. In this research, multiresolution segmentation algorithm was used to create the image objects. By delineating objects from images, object based image analysis enables the acquisition of a variety of additional textural and spatial features, which are helpful in improving the accuracy of polarimetric image classification and also reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects. To extract optimal features, it is essential to use an appropriate analysis tool. For this purpose, in this paper SEaTH analysis tool was used. In this method, by using Jeffries-Matusita’s measure, the features are extracted as the optimal features in an appropriate separation of the probability distribution function for the training samples belonging to different classes. The proposed method was applied to the RADARSAT-2 imagery of an urban area in fine quad polarimetric mode. This imagery was selected in order to include a variety of land cover categories i.e. urban, water, bare soil and vegetation. The results demonstrated that the accuracy of OBIA approach is higher than support vector classification using grid search and all input features. Finally, we demonstrate that classification accuracies are significantly higher by simultaneously optimization of SVMs parameters and input feature subset on Polarimetric imagery.}, Keywords = {Synthetic Aperture Radar, PolSAR, Support Vector Machine, Genetic Algorithm, Object Based Image Analysis}, volume = {5}, Number = {1}, pages = {127-138}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-155-en.html}, eprint = {http://jgst.issgeac.ir/article-1-155-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {Z.Masoomi, and M.S.Mesgari,}, title = {Spatial modeling of urban land use change using NSGA-II algorithm and clustering of the Pareto-front for urban dynamic plans}, abstract ={In urban space, the need to different facilities and diverse land uses increases continuously.  Continuous changes in the demands of the citizens results in rapid and frequent land use changes. Therefore, dynamic characteristics of urban environment should be considered in urban planning. On the other hand, land uses have different effects on each other. In other words, any change in the land use of a parcel or zone, will results in tendency of its neighboring parcels for land use change. Therefore, proposing of new land use arrangements after any occurred land use change could be a proper response to such tendencies. The main goal of this study is to propose a method, based on GIS and NSGAII optimization algorithm, for generating optimum land use arrangements after any occurred land use change. Usually, the output of a multi-objective optimization algorithm is a collection of optimum solutions. Selection of appropriate solution from such a collection needs extra efforts and processes. Therefore, another goal of this research is to use an appropriate clustering method that helps the user to select the most preferred solution. With such a method, the decision maker can introduce his planning priorities, perceive the resulted scenario and select accordingly. In this research, ant colony clustering algorithm is used for clustering, first because of its high speed, and then because the representative of the clusters are selected from the Pareto front solutions.  In this research, for modelling of the aspects of land use change and its factors, four objective functions are considered, which are: the maximization of land use compatibility, the maximization of land use dependency, the maximization of land use suitability, and the maximization of land use compactness. Finally, the providence of per capita for different land uses is considered as constraints. The ant colony clustering algorithm is used for clustering of the found solutions (land use arrangements). The developed method is implemented and tested using the data related to some districts of region 7 of Tehran. Different evaluations are considered and carried out for the results of optimization. These include the convergence trend, repeatability test, and the comparison of the previous land use arrangement with the optimized ones. In the resulted optimized land use arrangements, the levels of objective functions are much better than the previous arrangement. Furthermore, the required per capita for different land uses are much better satisfied.  The highest improvement in the objective functions is 36%, which is related to land suitability. In addition, the required per capita is improved by 18.5% of. The results of clustering using ant colony clustering algorithm are compared with those of K-means and Fuzzy K-means. The comparison showed that the ant colony clustering algorithm is faster. In addition, the results of this clustering method are exactly the original solutions of the land use arrangement optimization. Finally, the developed method can help urban planners and decision makers to correct and change the detailed urban plans according to any occurred land use change. One of the limitations of the detailed urban land uses plans is that they are not flexible and cannot opt to the deviations from the plan. This research is one step in the development of a general approach to dynamic urban planning. Such a planning approach can respond to the continuous and dynamic changes of the land uses in urban space.}, Keywords = {Urban Land use Planning, Land Use Change, Multi Objective Optimization, Clustering, GIS, Decision Support}, volume = {5}, Number = {1}, pages = {139-157}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-144-en.html}, eprint = {http://jgst.issgeac.ir/article-1-144-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.SabzaliYameqani, and A.A.Alesheikh,}, title = {Development and evaluation of a walkability index (Case Study: districts of the Ghom city)}, abstract ={The growing trend of urbanization has led to an unsustainable environment that become one of the main concerns of today’s city managers. Nowadays, the role of transportation in sustainable urban development is very clear and undeniable. On the other hand, the need for sustainable transportation reinforces the idea of using non-motorized transportation. Non-motorized transport is a form of entertainment and recreation, and can provide the possibility of cheaper trips. The walkability index is one of the important issues in the non-motorized transportation. The determination of walkability index leads to the recognition of environment as a factor affecting the extent of encouragement of individuals to walk and ride bicycle. As a result, one can improve the existing conditions so as to enhance the physical activity level of individuals in a society. The existing methods of determining the walkability can be summarized into three types: 1- objective 2-subjective, and 3-experts’ field studies. The objective methods determine the extent of walkability index through GIS data. The subjective methods measure the walkability index with the help of public opinions (i.e. survey). The field-study methods collect the experts’ viewpoints and comments so as to measure walkability index. The objective methods usually use certain parameters such as population density, dwelling density, land-use diversity, access to stores and urban services, connectivity, intersection density and network density. All across the urban planning community, much effort is currently being put into providing safe and friendly environments that encourage walking in cities. The main goal of this study is to develop a simple yet efficient measure that captures properties of pedestrian friendliness, which is region-specific, and that can be particularly useful to urban planners and metropolitan planning organizations. In this research, the development of the walkability index in the city of Qom has been introduced. Parameters affecting the walkability index were identified and assessed. These parameters include: Land-use diversity, population density, intersection density, network density, access to public transportation, access to religious sites, access to primary schools and kindergartens, access to secondary to pre-university schools and parking sites. A noteworthy point considered in all of the parameters is the normalization of results (i.e., keeping its values between 0 and 1). Then, the ranking of the alternatives was produced using AHP-TOPSIS method in five age/sex classes. The target population of this research is people who are willing to walk for daily tasks and recreation. The result of this study showed that the districts of Sarehoz, Razaviye, Labechal, Nobahar, Pamenar, Chehel Akhtaran, Sarbakhsh, Niroghah, Zad, Zandian, Emam hosein town, Vadiassalam, Darvaze Rei and Ali Abad Sadegan have the best condition among other districts. Also, the districts such as karbaschi garden, Berasun, Yazdanshahr, Shah seiied ali, Fatemiye town, Noghatar, Kasegarha, Panzdah khordad and Bajak two have the worst condition. Generally, the central districts have better conditions than the others. Final evaluation of results by five Likert questionnaire (strong, good, moderate, weak, and poor) were done. Finally, the result showed that the access to parking has the weakest point and the land-use diversity has the highest effect on the proposed walkability index.}, Keywords = {Walkability index, TOPSIS, Non-motorized transportation, Sustainable urban development, GIS}, volume = {5}, Number = {1}, pages = {159-174}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-236-en.html}, eprint = {http://jgst.issgeac.ir/article-1-236-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {Y.JouybariMoghaddam, and M.Akhoondzadeh, and M.R.Saradjian,}, title = {A Novel Method for Retrieving Land surface Emissivity from Landsat-8 Satellite Data Based on Vegetation Index}, abstract ={Land Surface Emissivity (LSE) is a significant parameter in many different land surface studies. It can be used as an index for analyzing the structure of the material. Furthermore, LSE estimation is a significant factor in the land surface temperature estimation from remotely sensed data. In this study we presented a novel operational algorithm for retrieving LSE from Landsat-8 thermal bands (i.e.: band 10 and 11) based on vegetation index (VI). The study includes three steps: I) building up simulated dataset for Landsat-8 bands II) threshold determination for VIs and correlation analysis between VIs and LSE III) derivation regression between LSE and Vis. First, the simulated dataset has been built up based on spectral library and spectral response function of Landsat-8. ASTER Spectral Library (ASL, http://speclib.jpl.nasa.gov) and Vegetation Spectral Library (VSL), which is published by system ecology laboratory at the University of Texas at EL Paso in cooperation with the colleagues in University of Alberta (http://spectrallibrary.utep.edu/SL_browseData), were used to build up simulated dataset. These Library contain directional hemispherical reflectance of the different type’s area.  Then the threshold has been determined for each VIs and correlation analysis has been done between each VIs and LSE. The correlation between the vegetation indices and emissivity values was analyzed. The vegetation indices that were tested include: the Simple Ratio, SR, the Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, Transformed Vegetation Index, TVI, Soil-Adjusted Vegetation Index, SAVI, Leaf Area Index, LAI and the Modified Soil-Adjusted Vegetation Index, MSAVI. The results of this analysis show that the correlation between VIs and emissivity is acceptable therefore these indices are used for retrieving LSE. The results show that the maximum correlation occurred between NDVI and Emissivity, also the minimum occurred for MSAVI. For determining the threshold in this study we assumed that the area can be separated into three categories, including bare soil area, vegetated area and partially vegetated (mixed area). Then the statistical parameters (max, min, mean and standard deviation) for each category (bare soil, vegetation and mixed area) were calculated and based on these parameters, threshold values were determined for each category. Finally, regression relations have been derived to estimate LSE based on VIs. Support Vector Regression, SVR, and least square method were used for this regression. The RMSE of regression is different for each VIs. However, this value is less than 0.0035 for all VIs. The minimum of them occurred for NDVI and TVI also the maximum is for MSAVI. The presented method was evaluated by using an independent dataset. The result shows that the RMSE of LSE for band 10 and 11 is less than 0.007 and 0.009 respectively. The presented method is robust for estimating LSE from Landsat-8 satellite imagery and also is simple and do not need any auxiliary data. For further study, local comprehensive dataset can be built up and also the effect of atmospheric parameters or dust on regression coefficients can be analyzed.}, Keywords = {Land Surface Emissivity ,Vegetation Index, SVR, Landsat-8}, volume = {5}, Number = {1}, pages = {175-187}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-216-en.html}, eprint = {http://jgst.issgeac.ir/article-1-216-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Mousavi, and J.Amini, and Y.Maghsudi,}, title = {Proposal speckle reduction algorithm for SAR images}, abstract ={The existence of speckle in Radar images is an inevitable occurrence. The speckle noise is a granular disturbance that models often as a multiplicative noise in single-channel SAR images. This noise that dependent on signal, introduces for variations of phase of returned signals that appears as point-point pattern. Being speckle, make complex more the explanation and analysis of images and also decrease the access to the image data, so it is important which appropriate speckle-reduction algorithm should be choosing. therfore, a section is allocated to the introduction of speckle noise model. It provides some important facts about how the speckle formed and explains which probably density function is followed by the amplitude and the intensity image. Several adaptive filtering methods have been discussed to deal with issue in this paper such as: Mean, Kuan, Frost, Lee, enhanced Lee and Gamma-MAP. Then according to statistic characteristics of speckles and texture characteristics of SAR images an adaptive speckle-reduction algorithm with size-changeable window based on relative standard deviation have been put forward. This new algorithm uses a moving window like other typical filters, but its window is divided to four smaller windows that every of them is called subwindow. The relative standard deviation of every subwindow is used as compare factor. If all subwindows are in homogenous region the mean filter is applied on whole initial window. And if any subwindow has the edge or high-frequency information, it must be omitted from proceeding process. The proposed filter is size-changeable because, if all subwindows are not accepted by the rules, the filter need to reduce the size of initial window to provide a region for filtering process. This paper benefits some worthy indices to demonstrate the ability of proposed filter against common filters such as equivalent number of looks (ENL), speckle suppression and mean preservation index (SMPI), edge save index (ESI), mean square error (MSE) and signal to noise ratio (SNR). To analyze the proposal algorithm and other common filters, used a simulated four-look SAR image and two real SAR images i.e. Flevoland dataset from AirSAR airborne SAR sensor and Oberpfaffenhofen dataset from ESAR airborne SAR sensor, respectively. The simulated SAR image is determined according to multiplicative model and gamma distribution. It is used to show a primary evaluation of proposed filter and other filters. In the first step, the results are satisfying. For example, the indices of standard deviation (SD) and ELN for the proposed filter are 39.53 and 192.59 that in comparison to other filters are gratifying and agreeable. In the next step, the all filters that are described in this paper are applied on Flevoland dataset. As the experience results show, the proposal algorithm has a satisfying performance in removing speckle noise along with very good saving edge characteristics, targets scene and mean of image toward usual speckle-reduction filters. For instance, the indices of ENL and SNR for area 1 of first image are 17.22 and 7.91, respectively, that are highest values between other common filters. To survey more precisely, the mean and the enhanced Lee filters are selected to compare to the proposed filter by second real dataset in the ways of ability to remove speckle, preserve edge and maintain point target.}, Keywords = {synthetic aperture radar (SAR), speckle filter, relative standard deviation}, volume = {5}, Number = {1}, pages = {189-202}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-207-en.html}, eprint = {http://jgst.issgeac.ir/article-1-207-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.SabzaliYameqani, and M.R.Malek,}, title = {Developing an Optimal Path Algorithm Based on Intuitionistic Fuzzy theory for Uncertain and Incomplete Network}, abstract ={Finding the shortest path from origin point to destination point is of vital importance in different cases.  In a network, the length of arcs could show the length of the path, time of the path, or any other parameter. A fuzzy shortest path has a variety of applications. Now suppose that there are arcs with no specified length, or with specified length that vary depending on other parameters such as traffic, accidents etc. Moreover, on certain occasions such as smuggling, security forces may doubt the weight of arcs. In such cases, the use of fuzzy shortest path would not be efficient. The Intuitionistic fuzzy set theory can be considered as a generalization of fuzzy set theory in which non-membership function is used in addition to membership function, independently. Note that in fuzzy theory, no difference is considered between presence of data or reasons in favor or against any given subject. In other words, if membership function of an element be half from the fuzzy set, we cannot infer that information was little or that negative and positive reasons were provided with the same amount. Whereas the Intuitionistic fuzzy set and logic is capable of overcoming a number of the limitations of the fuzzy algorithm theory such as supporting doubts and uncertainty. On the other hand, due to the fact that one of the present issues in the graph is finding the shortest path in terms of uncertainty and lack of adequate information of distances. In this paper, the shortest path of Dijkstra algorithm is expanded for the graph with Intuitionistic fuzzy arcs having incomplete data. In this article, two problems with corresponding solutions are presented. The first challenge is about combining the arcs solved by using triangular Intuitionistic fuzzy numbers. The second problem concerns the method of comparing the arcs. To compare the arcs, there are numerous ways including utilizing centroid, maximum and minimum sets, integral values etc. Finally, integral values method was implemented. The reason for using this method is capability to differ between state of decision-maker like optimism and pessimism. So one can change equal inputs accordance to different condition to give different outputs. In this regards, we provide a numerical example of a road network. This network includes 25 nodes and 46 arcs. It is assumed that the value of arcs is triangular Intuitionistic fuzzy numbers as noted above. Then, the algorithm was tested on the network and was compared with the conventional fuzzy method.  Finally, the result of algorithm has been compared with the figures and tables and presented difference of the fuzzy and intuitionistic fuzzy paths. It should also be noted that in the case of information lack and algebraic uncertainties abound, Intuitionistic fuzzy logic will be useful, bearing more appropriate results compared to cases done with fuzzy logic. That is because the use of this algorithm allows us to analyse the possible routes pessimistically, cautiously, optimistically and moderately. Hence, information and lack of information as well as doubts and uncertainty will also be taken into account. As a result, the use of this algorithm provides results that are more adaptable to the given condition to be implemented by the decision maker.}, Keywords = {intuitionistic fuzzy graph, shortest path problem, Dijkstra algorithm, Intuitionistic fuzzy number, Uncertainty}, volume = {5}, Number = {1}, pages = {203-213}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-151-en.html}, eprint = {http://jgst.issgeac.ir/article-1-151-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {Y.JouybariMoghaddam, and M.Akhoondzadeh, and M.R.Saradjian,}, title = {A Split-Window Algorithm for Estimating LST from Landsat-8 Satellite Images}, abstract ={LST and LSE are two significant parameters in climatic, hydrologic, ecological, biogeochemical, and related studies. LST is an important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control index for climate models. Emissivity, is an indicator of land-cover type and resources, and also a necessary element in the calculation of LST from remotely sensed data. The main purpose of this paper is to present an operational algorithm to retrieve the Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from Landsat-8 satellite images. The proposed algorithm is Split Window (SW) with band 10 (10.6 – 11.19 &mum) and band 11 (11.50 – 12.51 &mum) of Landsat-8 in thermal infrared range. Also for LSE mapping, the Normalized Difference Vegetation Index (NDVI) method has been suggested. This study contains two main steps: first, emissivity values of bands 10 and 11 are calculated and NDVI threshold values have been determined to separate the bare soil, fully vegetated and mixed areas from each other. Then, by using a regression relation, the values of the emissivity of the bare soil samples and mixed area have been derived. A constant value of emissivity is also used for the fully vegetated area. For a regression relation and a constant value in this study, reflectance of Landsat-8 bands has been simulated based on using two different spectral library data and relative spectral response function of Landsat-8 thermal wavelengths. ASTER Spectral Library (ASL, http://speclib.jpl.nasa.gov) and Vegetation Spectral Library (VSL), which is published by system ecology laboratory at the University of Texas at EL Paso in cooperation with the colleagues in University of Alberta (http://spectrallibrary.utep.edu/SL_browseData), were used to create simulated dataset. For validation of this step according to the lack of accurate methods for retrieving LSE from Landsat-8 imagery, the method can’t be validated with real data. Therefore, the test simulated data, which are selected randomly from simulated data, were used for validating the method. In the second step, three simulated datasets have been used. One of them for obtaining the SW coefficients and others for validating the proposed SW algorithms. The simulated datasets should include brightness temperatures, surface temperatures, emissivity and atmospheric parameters (atmospheric transmission, upwelling and downwelling radiance) for the TIRS bands. For this purpose, for each Landsat-8 TIRS band (i.e.: band10 and band11) brightness temperatures are obtained from the RTE by inversion of the Planck’s law. Surface temperatures were chosen based on the temperature of the first layer of the atmospheric profiles (T0) as T0 &minus 5°K, T0, T0 + 5°K, T0 + 10°K, and T0 + 20°K. The emissivity was extracted from spectral library and the atmospheric parameters have been simulated using the MODTRAN for the standard atmospheric profiles of MODTRAN (including: tropical (TRO), mid-latitude summer (MLS), mid-latitude winter (MLW), sub-arctic summer (SAS), U.S standard (USS), sub-arctic winter (SAW)). The SW algorithm coefficients for Landsat-8 were calibrated. SW algorithm coefficients were retrieved by using the least square approach based on the simulated data. Finally, this SW algorithm was tested with three datasets: simulated data, real data and satellite data. Results show that the RMSE value retrieved from the SW algorithm is equal to 1.21°k, 1.76°k and 1.03°k respectively for the three datasets. Therefore the results indicate that the proposed SW algorithm can be a suitable and robust method to retrieve the LST map from Landsat-8 satellite data.}, Keywords = {LSE,LST,split-window,MODTRAN,Landsat-8}, volume = {5}, Number = {1}, pages = {215-226}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-225-en.html}, eprint = {http://jgst.issgeac.ir/article-1-225-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.A.Heidari, and R.A.Abaspour,}, title = {Chaotic Evolutionary Path Planners for Trajectory Planning of Autonomous UAS}, abstract ={Unmanned aerial systems (UAS) are one of the latest technologies utilized in the hazard management and remote sensing. Nowadays, tendency in the development of UAS is toward autonomous navigation or hybrid tasks. In this context, development of comprehensive, efficient methodologies for path planning, control and navigation of UAS can be regarded as one of the fundamental steps for the development of autonomous systems. Up to now, different planning algorithms have been proposed in the specialized literature in order to enrich the framework of autonomous navigation of unmanned aerial systems. However, few efforts have been devoted to design new chaotic path planners for determining the optimal trajectories of these aerial systems in urban areas. An effective path planning technique can attain mission aims with respect to various restrictions of the UAS and less computational time. Chaos theory is one of the most studied theories with different applications in engineering and technology. Most of the natural processes demonstrate chaotic behavior such as black hole and clouds. Past researchers showed that if an evolutionary algorithm be hybridized with chaos, its performance will have improved, considerably. However, most of the evolutionary algorithms are inspired from nature, but all of their steps are random based motions. But nature is not either completely random based or chaotic. Hence, the combination of these theories should be more realistic. With this regard, evolution and chaos are related to each other narrowly in most of the complex natural systems. It is evidenced that some of the chaotic signals can alleviate the premature convergence problem of the evolutionary algorithms in tackling optimization problems. In this article, first, UAS path planning is modeled as a 3D constrained optimization problem. In this modeling, the aim is the optimization of path, fuel and safety with respect to different restrictions. After scheming and suggesting of general planning framework, UAS path planning problem is investigated by comparative study with regard to the studied scenario. For this aim, evolutionary planner is implemented in order to minimize the flight height, path length and energy consumption considering different restrictions such as safe altitude, turning angle, climbing slope, gliding slope, no fly zones and mission map limits. Then, a comprehensive model is employed to describe route-planning task, and then, based on the hybridization of chaos theory with evolutionary computing, four new evolutionary optimizers are developed. Hence, this paper developed four chaotic optimizers including particle swarm optimization, differential evolution, imperialist competitive algorithm and artificial bee colony technique based on 14 chaotic signals. In the rest of this paper, analyses, and extensive performance evaluation of the designed trajectory-planning approaches are performed according to the success rate results, precision and quality of the results, CPU running times, and convergence speed. The results show that the proposed framework can be utilized in represented scenario as an effective path planner. Proposed strategies are capable to compute the optimal paths more efficiently in comparison with the standard algorithms. From the results it is known that the chaotic differential evolution with logistic map can outperform the other compared algorithms.}, Keywords = {Path Planning, Unmanned Aerial Systems, Chaos Theory, Evolutionary Computing, Differential Evolution, Imperialist Competitive Algorithm, Particle Swarm Optimization, Artificial Bee Colony Algorithm}, volume = {5}, Number = {1}, pages = {227-240}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-208-en.html}, eprint = {http://jgst.issgeac.ir/article-1-208-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Saber, and F.Samadzadegan, and H.Zahmatkesh,}, title = {Design and Development of a Fire Detection System Using Chaining of Geoprocessing Services}, abstract ={Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementation to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in SOA to become a widely accepted solution for processing remotely sensed data on the web. This standard is a generic interface without any restriction on supporting any specific process. It is possible to integrate different geoprocessing services in an individual WPS service and expose it as a single web service. This paper presents an architecture design based on OGC web services for automated workflow of acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks are represented using web-based processing of remote sensing imageries utilizing MODIS data. As a framework for this application, we consider a generic architecture, which consists of several components. These components are divided into layers according to the types of functionality they provide. A core module is provided to manage the interactions between different layers and components. To achieve the interoperability issue, OGC standard interfaces are utilized for storing, processing and representing remote-sensing data. A workflow of distributed processes are used for hot pixel detection using MODIS data to implement the feasibility of the proposed architecture. A composition of WPS processes, in a centralized opaque pattern, is proposed as a WPS service to extract fire events. The workflow includes several processes such as pre-processing of data, radiance number calculation, brightness temperature process, fire event rules computations. The paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The usage of available data and processing resources on a flexible infrastructure as a composite service is one of the benefits of service oriented architecture that provides a higher level of functionalities and applications. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. For the implementation of described system, open source web service software and java technology have been used. OGC standard interfaces of data access services, Web Coverage Service (WCS) and Web Feature Service (WFS), have been used to store and retrieve required data in a standard way. To notify the realized events of fire hazards to the authorities the standard implementation specification of OGC, Web Notification Service (WNS), was used to provide alerts in different protocols of communication. A GeoPortal client was developed to manage data, metadata, processes, and authorities alongside the mapping of results of fire event detection processes on satellite imageries. Investigating feasibility and benefits of proposed framework exposes that this framework could be extended and reuse the components of this architecture for wide area of geospatial applications specially disaster management and environmental monitoring.}, Keywords = {Web Service Chaining, Service Composition, Web Processing Service, Disaster Management, Fire Detection}, volume = {5}, Number = {1}, pages = {241-256}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-198-en.html}, eprint = {http://jgst.issgeac.ir/article-1-198-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {K.Parvazi, and J.Asgari, and A.R.Amirisimkooei, and B.Tajfirooz,}, title = {Determination of difference between datum and reference ellipsoid by using of analysis of altimetry datas of Topex/Poseidon ، Jason-1 and observations of coastal tide gauges}, abstract ={In traditional hydrographic surveys, chart datum is usually determined using spectral analysis of coastal tide gauge observations based on the lowest water level. Due to the variation of tidal amplitude and phase components in different locations, such chart datum is only valid in coastal areas around the tide gauge stations it is then hardly accurate when a few tens of kilometers away from the tide gauge stations in the off-shore areas. This study models the separations between the elliptical reference WGS84 and the chart datum using the data collected by the satellites Topex/Poseidon and Jason-1 for the periods of 1992-2002 and 2002-2008 in Persian Gulf and the coastal tide gauge data of the Oman Sea. The major advantages of the technique used in this study are highlighted as follows. The satellite altimetry observations on the shores and also in the shallow water is not of good accuracy and precision. An appropriate solution to this problem is to use coastal tide gauge observations near the time series generated from the satellite observations. On the other hand, chart datum determination based on the coastal tide gauge observations is suitable only for the areas close to the coastal tide gauge stations. However, due to the amplitude and phase variations of the tidal components in different parts of the sea, the accuracy of such a chart datum is not appropriate for the areas that are far away from the tide gauge stations. In this study, the satellite altimetry observations are combined with the data obtained from the coastal tide gauge stations. Due to the existence of point-wise time series in the study area and also creation of the “quasi tide gauge” points using the satellite altimetry observations, both of the problems mentioned above (i.e. weakness of the satellite altimetry observations and the chart datum determination based on the use of the coastal tide gauge observations only) can be solved. This will then lead to higher accuracy of the chart datum determination in the entire area. To achieve higher accuracy, observations of the tide gauge stations are also used. It is because the distances between the tide gauge stations and the time series obtained from the satellite observations are considerably large (sometimes more than 20 km). Therefore, this combination will lead to the establishment of a regular and continuous network of tide gauge observations in the entire area having acceptable accuracy. To determine this model, sea level variations due to the tide, polar motion variations, plates tectonic movements and all the factors affecting the potential tide with significant components (M2, S2, K1, O1) and even less important components such as signals with the period of 14 days, monthly, semi-annual, annual, 8.5 year and 18.6 year are considered in the case study area. It is also highlighted that because the satellite altimetry observations are only available on periods of 9.915 days, the high-frequency tidal signals cannot be detected using these data. Therefore, these frequencies are also included into the functional part of the model. Based on the above strategy, the separation between the elliptical reference and the chart datum has been computed by comparing the tide gauge data and the satellite altimetry data for the period of 2002-2005.}, Keywords = {topex/posiedon , jason-1 , satellite altimetry, coastal tide gauge, chart datum, persian gulf and oman sea}, volume = {5}, Number = {1}, pages = {257-269}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-189-en.html}, eprint = {http://jgst.issgeac.ir/article-1-189-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.A.Safari, and S.Kalantarioun, and H.Amini,}, title = {An approach to the spectral analysis of the Jason-2 satellite altimetry observations based on stationary time series Case study: spectral analysis of instantaneous sea level of the Caspian Sea}, abstract ={With the advent of the satellite altimetry in 1973, new scientific applications become possible in oceanography, marine sciences and Earth-related studies, and it was made possible to monitor the sea level with high accuracy and tidal modeling in a global scale. Advances in the sensors technology and different satellite altimetry missions in the recent years led to a great evolution in geodesy and the gravity field modeling studies. The oceanic tide is a periodic phenomenon of rise and fallen of the sea level that has emerged from the combination of waves with different periods. It is governed by the gravitational action of the solar system bodies essentially the moon and the sun, it translates by a transport of water masses. Tide phenomenon is of particular significance among researchers in different scientific societies such as Geodesy, Geophysics, and Oceanography. Since tide is one of the most effective factors on instantaneous sea level fluctuations, it is necessary to know the status of tidal in all offshore projects. So, we can have wider and more optimal exploitation of marine resources with known of the main components of the tide, and finally, the tide phenomenon and sea levels could be predicted with the exact knowledge of the tidal frequencies. Several methods, including Least squares method, Fourier analysis, and statistical methods, have been developed to determine the tidal frequencies. Fourier analysis is a convenient and efficient mathematical tool for modeling the behavior of a periodic phenomenon. In this study, main constituent frequencies of time-series and finally, tidal frequencies in Caspian Sea are determined using the Fourier analysis method relying on the concept of stationary time-series. In this way, first, for a closer look at the data and better visibility of other fluctuations in time-series, the trend component is removed from the data using the Fourier seriese. Next, the frequencies that make up the time-series were identified by the Fourier analysis and least squares method, and as mentioned, the concept of stationary time-series is used to find the main components of the tide in this study. Data from the altimetry satellite Jason-2 from 2008 to early 2014 is analyzed to form the instantaneous sea level time-series in 7 points in Caspian Sea that indicates large instantaneous sea level fluctuations. For   time-series, the suitable model for tide modeling can be determined by the following equ ation: where  is amplitude,  is frequency,  is related phase, and  is the number of frequencies contained in the model. A discrete set of  frequencies can be achieved by  in a time-series with equidistant data, where  is the time of the first observation and  is related to the last. Nyquist Frequency is calculated as  . So, the above equation can be written as: So, the amplitudes related to each frequency can be calculated using a least squares adjustment and much more effective frequencies contained in the signal can be identified based on these amplitudes. This analysis uses the concept of Aliased Frequency to calculate the main tidal components in 7 points in Caspian Sea, including: SSA، SA، S2، M2، MF، MM، S4 and M6 component. Finally, spectral analysis is used to study the effect of Volga River on the Caspian Sea level changes.}, Keywords = {: Least Squares method, Fourier analysis, satellite altimetry, tidal frequencies, Nyquist Frequency, Aliased Frequency}, volume = {5}, Number = {1}, pages = {271-285}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-77-en.html}, eprint = {http://jgst.issgeac.ir/article-1-77-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Jafari, and M.Mashhadi-Hossainali,}, title = {Application of Network RTK Positions and Geometric Constraints to the Problem of Attitude Determination Using the GPS Carrier Phase Measurements}, abstract ={Nowadays, navigation is an unavoidable fact in military and civil aerial transportations. The Global Positioning System (GPS) is commonly used for computing the orientation or attitude of a moving platform. The relative positions of the GPS antennas are computed using the GPS code and/or phase measurements. To achieve a precise attitude determination, Carrier phase observations of GPS requiring the phase ambiguity resolution has been utilized. The more accurate the coordinates, the more accurate the attitude parameters will be. Attitude parameters are derived from the computed coordinates. Here, attitude parameters are computed by carrier beat phases of four single frequency GPS receivers. The problem of GPS attitude determination is an ill-posed problem if only GPS carrier phases are used. This is because the number of unknown parameters is always larger than the number of observations when the relative positions of the GPS antennas are computed. In this research, carrier beat phases of four single frequency GPS receivers are used to determine the orientation of a platform whose attitude parameters are already known. Observations are made for 10 minutes. In this research, two sets of constraints are used to fix the rank deficiency of the problem. The first consists of the Real Time Kinematic (RTK) coordinates of the GPS antennas. Fixed antennas to the moving body help add five additional constraints (second set) to the problem. These constraints increase the redundancy and make the least-squares estimation of the attitude parameters possible. Since the application of regularization methods contaminates the solution with regularization errors, application of the proposed constraints is superior to regularization techniques. This is practically shown through the comparison of the computed attitude parameters, a similar set of results which is derived using the Moore-Penrose algorithm as a regularization technique, and the reference values of these parameters which are provided through an independent research. According to the obtained results, 59 seconds is required to fix the ambiguity parameters. In other words, to reduces the accuracy of the float ambiguities to less than 1.0 cycle, their initial estimate should be updated by the next 58 measurement epochs. Then the ambiguity parameters are rounded to their nearest integer number. On average, the least squares estimate of the yaw parameter is 51.7000 with the standard deviation of ±0.01710. The average estimate of pitch is 39.1680 with the standard deviation of ±0.01540. Finally, on average, the least squares estimate of the roll is 26.1530 with the standard deviation of ±0.01370. Computed attitudes have been compared to their known values. By the new definition of the body frame given in this study, least-squares estimation of the attitude parameters would be possible even if only three GPS antennas are used. Computing the transformation parameters between the new and conventional body frames, attitude angles can be transformed to any conventional frame. The proposed method of this research is superior to the others. The computed biases represent the integrity of determination and corroborate usage of inner constraints and weighted parameters to resolve the rank deficiency of the problem.}, Keywords = {GPS Attitude Determination, RTK Network, Inner Constraints }, volume = {5}, Number = {1}, pages = {288-297}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-146-en.html}, eprint = {http://jgst.issgeac.ir/article-1-146-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.Masjedi, and Y.Maghsoudi, and M.J.Valadanzoej,}, title = {A New Method for Contextual Classification of Polarimetric SAR Data Based on Combining SVM and MRF}, abstract ={Forest species classification has become increasingly important due to changing environmental conditions and the need to sustainably manage forest resources. They have an important role in the global hydrological and biochemical cycles. Remote sensing has been shown to be valuable for forestry applications such as monitoring, forest inventory, biophysical variable estimation, species discrimination or classification. In this regard, polarimetric synthetic aperture radar (PolSAR) data can provide information on the structure of forests. Moreover, recent studies show that image classification techniques, which use both scattering and spatial information, are more suitable, effective, and robust than those that use only spectral information. The main motivation of this paper is presenting an appropriate contextual classifier for PolSAR data. Relating the posterior Markov random field (MRF) energy function to the support vector machine (SVM) classifier, the proposed method takes advantage of both the parametric and nonparametric classifiers which efficiently combines SVM and Wishart classifiers. The proposed contextual image classifier adopts the ICM approach to converge to a local minimum and represent a good tradeoff between classification accuracy and computation burden. In proposed method, the computational cost of the training stage is exactly similar to that of training stage in SVM. However, for classifying test pixels, computational cost of the proposed method is the sum of those of SVM, Wishart, and also MRF. The method allows taking into account various types of features. In particular, the features obtained directly from original data, the features which are derived using the well-known decomposition methods, and the SAR discriminators are used as input features. Moreover, the covariance matrix of the PolSAR data and the Wishart distribution are used to compute the MRF energy function. The proposed method modifies the decision function and the constraints of SVM based on the integration of contextual information. Selection of the appropriate features and optimization of requiring parameters perform simultaneously using genetic algorithm (GA). In this study, two Radarsat-2 polarimetric images acquired in the leaf-off and leaf-on seasons are used from a forest area. A total of six classes, including white pine, red pine, poplar, and red oak are cinsidered. Two other classes in this study are water area and ground vegetation region. In this study, the selection of the appropriate features and optimization of requiring parameters are performed simultaneously using GA. Then, the proposed algorithm is compared with the Wishart, Wishart-MRF (WMRF), and SVM as the baseline classifiers. Comparison of the accuracy of the proposed method with baseline methods is performed. The results show that this algorithm allowed approximately 16%, 11%, and 7% increases in overall accuracy with respect to the Wishart, WMRF, and SVM classifiers, respectively. Moreover, proposed method allows 25.29%, 19.74%, and 11.76% increase in average accuracies of forest species with respect to the Wishart, WMRF, and SVM classifiers, respectively. This demonstrates the efficiency of the proposed method for classification of forest species. Also, the results show that, incorporating contextual information into proposed method significantly improves the spatial regularity of the classification results and reduce the sensitivity to noise or speckle.}, Keywords = {Polarimetric Synthetic Aperture Radar, Wishart distribution, Support Vector Machine (SVM), contextual image classification, Markov Random Field (MRF)}, volume = {5}, Number = {2}, pages = {1-16}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-218-en.html}, eprint = {http://jgst.issgeac.ir/article-1-218-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Ghanbari, and V.Akbari, and A.A.Abkar, and M.R.Sahebi,}, title = {Minimum-Error Thresholding for Unsupervised Change Detection in Multilook Polarimetric SAR Images}, abstract ={In this paper, we propose an unsupervised method for change detection in polarimetric synthetic aperture radar (PolSAR) images. The symmetric revised Wishart (SRW) is applied for measuring the similarity of two multilook complex (MLC) covariance matrices. The SRW produces a scalar feature image as an input into the automatic thresholding algorithm which is aimed to distinguish change from no change. In particular, the Kittler and Illingworth minimum-error thresholding method is generalized to model the non-Gaussian distribution of change and no change classes. Experimental results on bi-temporal simulated and RADARSAT-2 C-band PolSAR data confirm the effectiveness of the proposed method. The results of the real data also demonstrate that the multipolarization SAR improves the detection accuracy and lowers the overall error rate of the method compared to single-polarisation SAR. Introduction Multitemporal satellite remotely sensed data from a geographical area offers a great potential for monitoring and detecting changes in Earth’s surface (for e.g. damage assessment in natural disasters [1], monitoring the changes in agricultural areas [2], and glacier change detection [3]). Synthetic aperture radar (SAR) is an important instrument in remote sensing, providing measurements insensitive to the sun-light and atmospheric conditions. Furthermore, polarimetric synthetic aperture radar (PolSAR) sensors provide data with increased discrimination capability as compared to single-channel SAR and also insensitive to the sun-light and atmospheric conditions. Several change detection algorithms in SAR data has been developed in the literature, e.g., [4]-[5]. Unsupervised change detection is generally performed in three steps: 1. image preprocessing including co-registration, speckle filtering, and radiometric and geometric terrain corrections, 2. comparing SAR image pairs with a desired test statistic, and 3. finally, a thresholding method is applied to the test statistic to achieve the final change map. Fig. 1: the block diagram of the proposed change detection approach In the analysis of change detection in multilook complex (MLC) PolSAR images, the backscattered signal is represented by the so-called polarimetric sample covariance (or coherency) matrix.  In [2], the Wishart likelihood ratio test is proposed as a test statistic for change detection in multilook PolSAR images. Akbari et al. utilized the complex kind Hotelling-Lawley trace statistic as a new test statistic for change detection in multilook PolSAR images [18]-[19]. In [20], Ghanbari et al. applied the symmetric revised Wishart (SRW) distance in [8] as a test statistic for detecting the changes between two Wishart distributed multilook covariance matrices. Changes are finally detected by a decision threshold to the test statistic to distinguish change from no-change. In the present paper, the thresholding is performed using the generalized Kittler and Illingworth’s minimum-error algorithm (K&I for short) [10] on the SRW image. In the proposed method, the generalized Gamma distribution, denoted GΓD, is applied for modeling change and no-change classes in the SRW image. The GΓD was first proposed by Stacy [15] and has been widely applied in many fields, e.g., [14]. This distribution has a highly fixable form and good fitting capability to the histograms of change and no-change classes. Parameters of the probability density function (PDF) in this study are estimated using the method of log-cumulants (MoLC). This estimation method has been adopted in the analysis and processing of SAR images, e.g., [16]-[17].  The block diagram of the proposed unsupervised change detection method is presented in Fig. 1.}, Keywords = {Unsupervised Change Detection, Polarimetric Synthetic Aperture Radar Images, Kittler And Illingworth Minimum-Error Threshoding, Method Of Log-Cumulants, Symmetric Revised Wishart Test}, volume = {5}, Number = {2}, pages = {17-29}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-212-en.html}, eprint = {http://jgst.issgeac.ir/article-1-212-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {F.Mahmoudi, and M.Mokhtarzadeh, and M.J.Valadanzoej,}, title = {An Approach for Improving Change Detection in Agricultural Lands Using Georeferenced Multi-Temporal Image and Color Fusion Method}, abstract ={During the time, landcover and associated landuse patterns are changing very fast and the human factors play a major role in such drastic changes. Scientists have formerly attempted to identify the landuse altering processes and related environmental impacts. In the recent studies, evaluating the agricultural outputs and arable lands is regarded so important that their organization and management can be mentioned as a very critical factor for all countries. Nowadays, satellite images could be accurately processed, as an advanced technique in remote sensing, to determine the environment changes in a particular object of study between two or more time periods.Therefore, favorable results are achieved in recognition pattern-based remote sensing methods in order to achieve these global aims. Although there are various methods in photography and remote sensing for dealing with revealing changes, none of them can be considered as an optimum one completely. In the present paper an appropriate Supervised approach was proposed in odrer to identify changes in semi-urban areas based on both neural network algorithms and SVM (Suport Vector Matchene). To achieve this purpose, Landsat7 multi-temporal images are applied. In principle, this method unlike conventional ones, is not introduced to identify changes, but our method can be addressed as determining changes without comparing multi-temporal single-source images with each other and principally relying on color fusion ( fusing colors in different bands and creating a different color ) in the resulted single image which contains all the layers of two multi-temporal images. The main basic idea is to produce a multi-temporal single-source image using two images and then using color fusion and pattern recognition methods on the georeferenced single-sourced image, afterward, was produced a map for changed and unchanged regions, finally, was applied algorithm on image to provide the final change map. Simplicity and increases performance can be proposed as the advantages of this method. In fact, mixed collective color (color fusion) method with pattern recognition methods or classification methods and using them for rsulted reference single image the basis of this method in order to identify the changed and unchanged zones. Finally, our main idea was based on that after selecting training data from one single ( common data in both images ), use training data in unmodified and stable zones and remove the data which located in changed zone. In practice, after revealing the modified zones showing an overlap with training set data, the existing data in mentioned zones were removed. Finally, applying training data and conventional classification methods such as SVM and neural networks classes were identified and introduced and final map of changes developed. Our achieved results suggest that this approach is far better than traditional methods and significantly reduces training samples and increases accuracy (2.5 – 3 percent), pace and spectral information for performed classification.}, Keywords = {Supervised Classification, Neural Network Algorithms and SVM, Change Detection, Image Fusion Method, Georeferenced Multi-Temporal Image}, volume = {5}, Number = {2}, pages = {31-40}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-107-en.html}, eprint = {http://jgst.issgeac.ir/article-1-107-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.AslaniM.SaadiMesgari, and M.SaadiMesgari, and H.Motieyan,}, title = {Using Genetic Programming and Sensitivity Analysis for Improving Spatial Modeling in a GIS Environment (Case Study: Mineral Potential Mapping)}, abstract ={Extraction of spatial relationships between georeferenced layers is one of the important objectives in spatial modeling in Geographic Information Systems (GIS). In last decades, a lot of techniques have been proposed for spatial modeling. Among them, Computational Intelligence techniques have been successfully employed in a wide range of spatial modeling. Most Computational Intelligence techniques automatically solve problems with requiring the user to know or specify the form or structure of the solution in advance. However, in most cases determining the structure of the solution in advance is difficult and may lead to inaccurate results. In order to overcome this challenge Genetic Programming (GP) which is a systematic, domain-independent method inspired by evolution is applied. In GP, user is not required to specify the structural complexity of the solution in advance but the algorithm tries to find an explicit relationship between the input and output. GP uses a tree-structure which captures the executional ordering of the functional components within a program: such that a program output appears at the root node; functions are internal tree nodes; a function;#39s arguments are given by its child nodes; and terminal arguments are found at leaf nodes. However, GP can have a tendency to find solutions that are biased towards the training set (Overfitting). In this research we proposed a new method for limiting the effect of overfitting in GP. Also, for achieving more accurate results we use multigene GP in which individual consists of one or more trees. At the final stage, Sensitivity Analysis (SA) which is the study of how uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input is used to determine what inputs, parameters or decision variables contribute more to the variance in the output of a model. There are three types of SA: 1-Screening SA, 2-Local SA and 3-Global SA. Screening SA methods are approximate but with low computational cost. When dealing with models containing large amounts of uncertain input factors, screening methods could be useful because they are able to isolate the set of factors with strongest effect on the output variability by very few model evaluations. A drawback of this feature is that the sensitivity measure is only qualitative which means the input factors are ranked in order of importance. Local SA looks at the local impact of each factor on the model output. The input variables are basically changed one at a time and the impact of this individual parameter perturbation on the model output is calculated using local sensitivity indices. A drawback of this feature is that the method does not work when the model is either nonlinear or several input factors are affected by different uncertainties. In global SA, both relative contributions of each individual parameter and the interactions between parameters to the model output variance are simultaneously evaluated by varying all input parameters simultaneously over the entire input parameters space.  Several types of global SA, such as partial rank correlation coefficient, multiparametric sensitivity analysis, Fourier amplitude sensitivity analysis (FAST) and Sobol’s method have been used successfully in different models. Among different methods of sensitivity analysis, Sobol’s and EFAST as the methods of variance-based are employed. The proposed method has several applications in spatial modeling issues. As a case study, the proposed procedures were applied to produce mineral potential map of Aliabad copper deposit. Results indicate that total field intensity criterion has the most effect and lithology has the minimal impact on the mineral potential mapping.}, Keywords = {Genetic Programming, Sensitivity Analysis, Mineral Potential Map, Geographic Information System}, volume = {5}, Number = {2}, pages = {41-53}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-97-en.html}, eprint = {http://jgst.issgeac.ir/article-1-97-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {E.KeshtehGar, and A.Sadeghi-Niaraki,}, title = {Ubi-Asthma: Design and Implementation of Asthmatic Patient Monitoring System in Ubiquitous Geospatial Information System}, abstract ={In recent years, the growth of population, increase in environmental pollution, and changes in human’s life style, a dramatic increase in the number of asthmatic patients has been observed. In addition, the Lack of suitable solution for the cure of asthma shows us the necessity of recent researches for controlling of the exacerbation of asthma in order to improve patients’ quality of life. Environmental condition in a spatio-temporal perspective enables us to do some predictive analytics in order to providing this ability to the asthmatic patients to have self-checking in addition to the medication by personal physician. Ubiquitous patient monitoring system which is enabled by geospatial perspective can assist us to provide such services to the asthmatic patients in both outdoor and indoor environments. In this paper, we monitor ubiquitously asthmatic patients in a Ubiquitous GIS environment by considering 8 important environmental asthma triggers including CO, NO, O3, PM10, SO2, Temperature, Humidity, and Pressure, and 4 medical records of current status of asthmatic patients. In fact, to achieve a better modeling with divided our research to two parts: User Model and Contextual Model. For implementation of Ubiquitous Asthma model (UbiAsthma), this survey is done by consideration of 30 patients in Tehran city. To develop our prediction model in UbiAsthma, at first we used VIKOR method to reclassified current patients’ medical classification to a novel and applicable classification for providing context-aware services. Secondly, after classification of patients, one patient is selected and the information related to FVC and FEV1 of the patient in 86 different locations within 47 days is collected.  The patient was equipped by CO pollutant sensor (MQ9) and Temperature, Humidity, and Pressure sensors collection by smart-phone in order to having of a real-time database for the patient’s trajectory. Also, we utilized O3, NO, SO2, and PM10 for all 86 locations as static database in our model. After data collection and manipulation, Artificial Neural Network (ANN) is used for doing predictive analysis for asthmatic patients’ status. The result of test and train of ANN method shows that UbiAsthma model has 0.0230 evaluation error in prediction of asthmatic patients’ status, which is a considerable output in our model.}, Keywords = {Ubiquitous Geospatial Information System, Asthmatic Patient Monitoring, Context-awareness, VIKOR, Artificial Neural NEtwork (ANN)}, volume = {5}, Number = {2}, pages = {55-66}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-262-en.html}, eprint = {http://jgst.issgeac.ir/article-1-262-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {F.Ghaderi, and P.Pahlavani,}, title = {Finding the Robust Multi-Modal Route by Integrating the Fuzzy-AHP with the Quantifier-Guided OWA}, abstract ={The purpose of Multi-modal Multi-criteria Personalized Route Planning (MMPRP) is to provide an optimal route between an origin-destination pair by considering weights of effective criteria, in which this route could be a combination of public and private transportation modes. In recent studies, the weighted linear aggregation rules were developed to calculate the impedance of links that were high tradeoff decision strategies. A decision strategy defines whether a user insists on satisfying all of his/her preferences regarding the selection of one route from a set of routes or he/she would be happy if the most of criteria would be satisfied. In this paper, a fuzzy analytical hierarchy process (fuzzy AHP) and quantifier-guided ordered weighted averaging (Q-OWA) operators were integrated to calculate the impedance of links. Fuzzy analytical hierarchy process (fuzzy AHP) weighting method by Saaty embeds fuzzy theory to basic AHP method. In AHP weighting method, a matrix-liked structure is considered by pairwise comparison between criteria with exact numbers. Despite the general popularity, the AHP method is not capable to consider the users’ ambiguity and the lack of clarity in their preferences. To solve this problem of the AHP method, the fuzzy AHP method was proposed to use fuzzy numbers rather than exact ones in pairwise comparisons. In this research, the triangular fuzzy numbers was used for these pairwise comparisons. To model a family of parameterized decision strategies, Yager introduced the ordered weighted averaging (OWA) operators. This method calculates the user’s risk taking and risk aversion, as well as enters them for selecting the final option. Quantifier-guided OWA is obtained by integrating the fuzzy linguistic quantifiers with the OWA operators. In this study, a class of relative quantifiers, called “Regular Increasing Monotone (RIM)” was used. The main characteristic of these methods is supporting the different decision strategies in calculating the impedances. In this method, the user determines the relative weights with fuzzy AHP method at first. Then by considering his/her desired decision strategy, impedance of the links were calculated. The proposed model can also propose the robust personalized route under the different decision strategies. By considering the different decision strategies, this model provides different routes. Then, by determining minimum and maximum impedance of each link, the model presents the robust personalized route. In this study, subway, BRT, bus, taxi, and walking transportation modes were considered for traveling between nodes. Moreover, time, length, and user’s bother of each transportation mode were considered as effective criteria. This model was implemented in an area in the center of Tehran. The considered area had 21 km2 and consisted of 2 BRT lines, 28 sweep bus lines, and 4 sweep subway lines, and totally more than 45 km of roads.  The proposed method was implemented for one of the most crowed path in our case study, i.e., a path from Baharestan square to Enghelab square. Initially, the devoted configuration wizard for the pairwise compression were presented to 45 users (9 users for any decision strategy) and they were asked to weight the criteria. Then, the relative importance of each criterion was calculated by considering the weights assigned within the pairwise compression matrix. Afterwards, they were asked to determine their desirable decision strategies, including “at least one”, “half”, “many” and “all”.  Results showed that on average 80.66% of the users with different decision strategies, selected the model proposed route as the best route}, Keywords = {Multimodal Multi-Criteria Route Planning, Decision Strategies, Fuzzy AHP, OWA Operators}, volume = {5}, Number = {2}, pages = {67-78}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-275-en.html}, eprint = {http://jgst.issgeac.ir/article-1-275-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {P.Pahlavani, and H.Amini,}, title = {Knowledge Extraction from an Adaptive Neuro-Fuzzy Inference System for Detecting Urban Objects, Case Study: Buildings and Trees}, abstract ={Nowadays, powerful detection systems, which the learning procedure of them is black box and is not available, have been widely used to classify data. However, the understandability of the acquired knowledge from these detection systems can significantly help operator in carrying out classification performance with high accuracy and precision. Hence, knowledge acquisition in a form of fuzzy rule set is an important issue in the image processing that causes to comprehend the classification methods appropriately and to improve them subsequently. The purpose of this paper is proposing a method to extract fuzzy rules in IF-THEN form via an Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification LiDAR data and digital aerial images. Detection of building and tree in urban areas needs to determine some features to perform the detection procedure; because classification algorithms decide about pixel entity based on its feature vector. These features can make the object separation possible by the textural, the spectral, and the structural characteristics. Nowadays, by increasing the number of the active and passive sensors, it is possible to record the textural, the spectral, and the structural characteristics of objects in different wavelengths by various approaches. In this paper, some potentially features were generated, and then optimal features were selected using the genetic algorithm. Using the selected optimum features, an ANFIS was used to recognize the objects accurately. In this regard, at first, the prepared training data was utilized as inputs of grid partitioning algorithm and a Sugeno fuzzy inference system with one output was generated by determining the type and the number of input membership functions, as well as theS type of output membership functions. Then, the grid partitioning algorithm figured out the best state of the membership functions after investigating the whole of the possible states. Afterwards, the training and checking data were entered into the generated ANFIS and during the training procedure, the final classifier was concluded to detect buildings and trees. Finally, by proposing a different fuzzy-based method and using the selected training data, as well as the output membership functions of the proposed ANFIS, a set of effective fuzzy rules were extracted. The proposed method has three main steps. In the first step, the tuned premise parameters (after training process) of inputs training data of ANFIS were extracted according to the mean values of the membership functions. In the second step, firstly, based on the number of membership functions of each feature, the total number of feasible fuzzy rules was determined. Then, for each training data, the fired values for all rules were computed. The rule that had the most effect in the process was chosen as the fired fuzzy rule of each training data related to the desired object class. In the third step, the fuzzy rules which has the importance more than a specified threshold in the classification procedure were extracted. The extracted fuzzy rules were considered and analyzed logically regarding the feature layers, and the results show the high capability of the fuzzy-based proposed method in extracting rules from the objects detection procedure.}, Keywords = {LiDAR Data, Digital Aerial Images, Fuzzy Rules, Grid Partitioning Algorithm, Adaptive Neuro-Fuzzy Inference System}, volume = {5}, Number = {2}, pages = {79-96}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-265-en.html}, eprint = {http://jgst.issgeac.ir/article-1-265-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.Ghafouri, and J.Amini, and M.Dehmollaian, and M.A.Kavoosi,}, title = {Random Fractals Geometry in Surface Roughness Modeling of Geological Formations Using Synthetic Aperture Radar Images}, abstract ={Some geological formations are more subject of weathering and alteration, so they are physically softer; in contrast, some other formations are more resistant against physical and chemical alterations, so they are harder and rocky on the ground surface. According to lack of geometric texture detection capability in usual optical images, the radar data must be undertaken. Because of the surface description capability of microwave remote sensing data, they are so much efficiently useful for morphological studies. Surface morphological modeling using Synthetic Aperture Radar (SAR) data requires topographic and micro-topographic surface model. Utilizing the ability of geometric pattern discrimination needs to relate dielectric and surface roughness parameters to radar signals back-scattering. Euclidean geometry is less capable in comparison to fractal geometry to describe natural phenomena. So far, some efforts are made to use fractal parameters in order to improve back-scattering model, but in none of them Euclidean geometry is not completely replaced by fractal geometry. Because of irregular nature of earth surface, geometry of the radar signal incidence on the earth surface is not Euclidean geometry and experimentally, fractal geometry describes it much better. Roughness of geological surfaces is an example of such natural phenomena. In literature, using fractal geometry in IEM has been performed by changing the computation process of some factors but, in all of such methods Euclidean geometry is the obvious rule of computation in IEM. In this paper, it is desired to replace the Euclidean geometry, basically by fractal geometry. Therefore, instead of conventional procedure of correlation length ( ) and rms-height ( ) calculation, two following equations are utilized: Where  and  are fractal surface parameters. According to adaptability of fractal theory with natural phenomena, it is supposed to generally have better results in IEM after having applied such improvements on the model input parameters. The methodology is implemented for PALSAR (ALOS satellite sensor) data analysis results of Anaran (between Dehloran and Ilam cities in Iran) geological formations. Field measurement of surface roughness using a total station and the data gathering performed on a grid of points. Thus, the digital elevation model (DEM) of the surface with sampling intervals smaller than the correlation length is formed. Surface roughness has been computed by IEM and get compared with field measurements on 20 selected pixels which show the most obvious improvement. In general, the behavior of the correlation function of the two polarization parameters are very close to each other. Although at sites 1 and 2 in some cases, lower standard deviation can be seen for Euclidean geometry, but mean standard deviation for fractal input parameters in IEM is considerably lower. Exponential ACF shows better for Site 1, and in contrary Gaussian ACF for Site 2 is more efficient; which confirms the fact that exponential ACF is suitable for soft surfaces and Gaussian one for rough ones. Due to irregular and fractal nature of the surface roughness, electromagnetic backscattering modeling of radar signals using fractal geometry calculates surface parameters closer to actual values. Gaussian correlation function is suitable for smooth and exponential correlation function is more appropriate for rough surfaces. The mean improvement in the use of fractal geometry for both polarizations hh and vv is about 50%. Comparison of three different sites with different levels of roughness provides similar results, and in particular, results improvement in areas with larger roughness parameters is more pronounced.}, Keywords = {Synthetic Aperture Radar, Geology, Integral Equation Model, Random Fractals Geometry}, volume = {5}, Number = {2}, pages = {97-108}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-228-en.html}, eprint = {http://jgst.issgeac.ir/article-1-228-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {S.Mehri, and A.A.Alesheikh, and H.Helali,}, title = {Developing a Spatial Knowledge-Based Approach to Detect Changes of Cultivation Fields}, abstract ={The dynamics of agriculture together with their in-depth influences on human and environmental conditions made the agricultural managers to look for new methods of information gathering. Such information must be accurate, effective and accessible on demand to lead the managers for better decisions. Agricultural changes are usually monitored in voluminous spatial-temporal databases.  Spatial-temporal analysis of agricultural fields can confirm the changes, and its type to provide useful statistics. One way to collect such statistics is through spatiotemporal reasoning. The process of identifying differences in the state of a phenomenon by observing it at different times is called change detection. In remote sensing, change detection is done by using satellite images that are taken in different time epochs. The images are compared based on the corresponding pixel values. The results of this process are highly depended on the used methods as well as the interpretation strategies. Several techniques of change detection in remote sensing have been developed. Most of these methods are based on ground sampling. Control points are also used to evaluate the final results. Because of dynamic nature of corps, ground samples are only valid for one cultivation season and this process (ground sampling) should be repeated in every cultivation season. This, itself, increases the cost and the time required for change detection. In most of these methods interpretation of results is a complex approach that needs lots of experiences in remote sensing. Therefore, selecting appropriate method of change detection is important. Evaluation of studies about knowledge-based systems and their applications in Geomatics showed that, proper use of knowledge can increase the accuracy of existing methods. Therefore, in this research a knowledge-based change detection method was developed to detect changes of farms and to identify the type of changes. The proposed method has two main stages: 1) creating a spatial knowledge base and 2) inferencing stage. The knowledge base of this method includes three sets of spatial (geometric), temporal, and spectral information. These rules are achieved by analyzing of rotation history, time series of satellite imageries, farms maps and other facts which can increase the accuracy of change detection. Mugan plain in northwest of Iran was selected as the study area to test the proposed methodology. Rotation history of wheat farms and time series of Landsat 8 imageries were used to execute the test. Different sources affect multitemporal satellite-image datasets such as atmospheric effects, the sensor’s stability and responsiveness. Because of the importance of homogeneity of multitemporal satellite-image datasets, especially in vegetation change detection by remote sensing data, a relative radiometric normalization method was used. To achieve the temporal stability in series of images, this step is taken. In this process the radiometric properties of an image time series is adjusted to match that of a single reference image. Implementation of the method in wheat farm, proved to 86 percent accuracy in change detection and 80 percent of accuracy in type of changes. In this method, the type of changes is recognized through spatial knowledge-based, and no needs were found for using rendition. By removing mixed pixels, the proposed method resulted in an increase in accuracy of change detection and in the identification of the type of changes up to 95% and 90% respectively. Therefore, it is concluded that the results of this method are more accurate than that of Normalized Vegetation Index (NDVI) differencing and Post-Classification. NDVI resulted in 70% accuracy while Post-Classification Comparison has 81% percent accuracy. In addition, the proposed method reduced field work for data collecting.}, Keywords = {Normalized Difference Vegetation Index (NDVI), Remote Sensing, Knowledge-Based Systems, Change Detection}, volume = {5}, Number = {2}, pages = {109-118}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-293-en.html}, eprint = {http://jgst.issgeac.ir/article-1-293-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Eslami, and A.Mohammadzadeh, and M.Janalipour,}, title = {Estimates of the Relative Changes of the Urmia Lake Using Fuzzy Classifier}, abstract ={Change detection has been one of the basic and crucial needs of management and exploitation of the environment and urban areas. Changes of the Urmia lake have been affected the life of the millions of the Iranian, Turkish and Azerbaijani people and their natural wildlife. Various methods and research studies have been developed to environmental change detection of the Urmia lake. The aim of this research study is to evaluate changes of Urmia Lake in the period of year 2006 until year 2011 by the Landsat 5 satellite images using the supervised fuzzy classifier. Therefore firstly, radiometric correction and image calibration have been applied to the both of the multi-year Landsat data. Then, bands number 4, 5 and 7 are selected as the references processing features according to the results of the previous works. Secondly, the multi-band differential features have been produced by subtracting the corresponding bands of the two Geo-referenced data of the mentioned years. Then two separate and multi-propose classification strategies have been applied to the produced feature space. Also obtained results are compared with the best outcome of the well-known SVM classification method.  Firstly "two class fuzzy" classifier method on the differential features has been applied. The obtained results provided changed and not-changed classes. Achieved results for overall and average accuracy are 96.25 and 96.50 percent correspondingly. The reached results for "two class fuzzy" classifier are compared with the outcome of SVM classification and are shown the increasing about 17.04 and 10.6 percent for overall and average accuracy correspondingly.     Because of the uncertainty, the word "changed" for some area has always been the big challenge. Sometimes the word "changed" can have many levels, which affects the management decisions. Also the changes can be divided as "little change", "mediocre change" and "more changed" classes, according to the different human attitudes. This kind of changes in the case study of the paper can be considered as "wet salt area", "dry salt area" and so on. Therefore secondly, the other fuzzy classifier is used to extraction of the not-changed, "little changed", "mediocre changed" and "more changed" classes. In the "four class fuzzy" classification method the training and test data are remained same as the previously mentioned "two class fuzzy" classification approach, while the defined fuzzy rules are alternated. The achieved classification results for the "four class fuzzy" method are shown the overall and average accuracy about 91.72 and 90.9 percent correspondingly. Moreover the class accuracy for the not-changed, little changed, mediocre changed and more changed classes are 96.14, 85.27, 94.70 and 85.88 percent respectively. The reached outcomes of the error matrix analysis are shown that the most correlation of the unchanged class is with the little changed class. Likewise the more correlation of the mediocre change class is with the more change class. The obtained result of the change detection for the "four class fuzzy" classification approach according to the human-oriented conceptual of relative changes in a phenomenon has the higher conceptual value.}, Keywords = {Fuzzy Classifier, Change Detection, Urmia Lake, Remote Sensing, Landsat}, volume = {5}, Number = {2}, pages = {119-130}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-305-en.html}, eprint = {http://jgst.issgeac.ir/article-1-305-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Ahmadlou, and M.R.Delavar,}, title = {Multiple Land Use Change Modeling Using Multivariate Adaptive Regression Spline and Geospatial Information System}, abstract ={The study of land use change is essential due to its significant effects on the environment and human life. Land use change modelers have mostly focused on the binary methods (e.g. urban and non-urban) rather than multiple land use changes methods. Also, most of the models used for modeling of land use changes are global parametric models (e.g. artificial neural network) and local non-parametric models (e.g. Multivariate Adaptive Regression Spline (MARS)) is rarely used to simulate multiple LUCs.  Local models split the data into subsets and fit distinct models on each of the subsets. Non-parametric models do not have a fixed model structure or model structure is unknown before the modeling. On the other hand, global models perform modeling using all the available data. In addition, parametric models have a fixed structure before the modeling. In this paper, we applied one of the well-known data mining tools, called multivariate adaptive regression spline, as one of the local non-parametric models with geospatial information system and satellite images to simulate urban and agriculture land use changes for northern part of Iran including cities of Sari and Ghaem Shahr over a period of 22 years during 1992 and 2014. Landsat images are the core source for information extraction and modeling of land use change in this research. Landsat images of 1992 (TM) and 2014 (ETM+) were used for modeling the urban and agricultural land uses changes. The spatial predictors considered for urban and agriculture modeling in this area were distance to urban areas, distance to agriculture areas, distance to roads, distance to water, aspect, and slope in 1992. After the modeling, a sensitivity analysis was performed on the effective parameters of the land use changes. The results of the sensitivity analysis verified that the most important factors were distances from agricultural and urban areas as well as elevation. To assess the model performance, the receiver operating characteristics (ROC) and total operating characteristics (TOC) were used. Considering multiple thresholds, ROC reveals how strong each threshold of the generated index is in diagnosing either presence or absence of a characteristic which results in a two by two contingency table without informing the size of each entries. While preserving the important information revealing by ROC, TOC gives size information of each entry. The area under the receiver operating characteristics curve for urban and agricultural land uses were 65% and 61.01%, respectively. Also, we have labeled thresholds for 0.67 and 0.40 in total operating characteristic curves for agriculture and urban gain to show four entries in the two-by-two contingency tables, respectively. These thresholds represent the probability of land use change for pixels in the suitability maps. According to the results, the percent of observations that are reference change and have been diagnosed as change by the model are equals to 36.8% and 67.06% for these thresholds, respectively.}, Keywords = {Land Use Changes, Multivariate Adaptive Regression Spline, Receiver Operating Characteristics, Total Operating Characteristics}, volume = {5}, Number = {2}, pages = {131-146}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-294-en.html}, eprint = {http://jgst.issgeac.ir/article-1-294-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {R.Arasteh, and M.R.Malek,}, title = {Mobile Agent Technology to Improve the Access to Spatial Resources at Clearing Houses}, abstract ={Nowadays, due to widespread use of maps and geospatial information systems in different organizations, different data layers collection methods for the preparation of required spatial data information, is of great importance. On the other hand doing in parallel in the preparation of spatial data and its storage in disparate sources often cause heavy costs. This has caused spatial infrastructure projects and clearing houses for communication between providers and the applicant. In this case the spatial data infrastructure is a communication network between the databases, systems and services, applications, technologies and policies that in different standard levels allows user access to data.  As well as the clearing house in spatial information technology means decentralized system of information that each of information provides a description of spatial data. Although different methods of distributed programming on the network, such as a code on demand, client server and Web services are provided , for distributed computing and creating clearing house, but available methods when increasing the volume of requests and information faced with problems. Increasing network traffic, creating bottlenecks and delays in network, problem in computing on client-side and the need for special plug-ins, reducing network security and access to intelligence information, the possibility of disseminating information and lack of commercial use, reducing network power and other problems are of weaknesses of the common methods of distributed computing. By investigating the mobile agent it can be present to achieve the required objectives of the clearing houses and reducing problems of existing methods. Mobile agent provide a strong alternative and pattern for network and distributed computing which has a clear difference with the current common methodologies and indicators such as, code-on-demand, client-server and Web service method, which led to the replacement of use of mobile agent with current distributed computing methods as well as the use of this system, along with methods to reduce the existing problems. In artificial intelligence the agent is anything that can understand their environment through sensors and relationships and environment is also react by defined incentives. In this research mobile agent with autonomous ability to move and make decision on network is used to create a clearing houses to achieve the maps and spatial information needed in related organization databases and avoid spending high costs to mapping and doing in parallel. In implementing this project a clearing house has been created based on the use of mobile agents in order to meet legal and real users’ needs to access spatial data stored locally across multiple computer systems. For this purpose 4 database related to organizations responsible for mapping including available metadata and maps were considered. Accordingly using presented in this study clearing house have characteristics of mobile agents such as network load reduction, stability and resilience and ability to work in heterogeneous environments. Practical implementation example for a SDI clearing house confirms above mentioned capabilities.}, Keywords = {Mobile Agent, Clearing House, SDI}, volume = {5}, Number = {2}, pages = {147-156}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-53-en.html}, eprint = {http://jgst.issgeac.ir/article-1-53-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.Habibi, and M.Motagh, and M.A.Sharifi,}, title = {Three-Dimensional Surface Displacements and Slip distribution of the 2003 BAM, Iran earthquake Estimated From Combination of Multiple-Aperture and Conventional Interferometry data}, abstract ={By using the ascending and descending Envisat satellite images and also Multiple-Aperture Interferometry (MAI) and Conventional Interferometry techniques, we have calculated the co-seismic deformations on the satellite line of sight and on the azimuth track for the BAM earthquake in 2003. Also the three Orthogonal components of the displacement field from this geodetic measurements were obtained. In order for calculating the fault geometry and the slip distribution on the fault plane then, we inverted the components by using Genetic Optimization Method and also the Analytical Model (Okada Elastic Half-Space). The maximum of the slip was about 2.5 meters along with the 30 K.ms BAM fault on the approximate depth of 4~5 K.ms and by inverting, we have estimated seismic moment (M0), N.m 1018× 7/6 , that indicates a shock on 6.5 Mw scale. By inverting and using the Bootstrap Statistical Method we have also estimated the 68% confidence interval  for model parameters.}, Keywords = {BAM Earthquake, Joint Inversion, Slip Distribution, Conventional Interferometry (Insar), Multiple Aperture Insar (MAI)}, volume = {5}, Number = {2}, pages = {157-165}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-90-en.html}, eprint = {http://jgst.issgeac.ir/article-1-90-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Sajadian, and H.Arefi,}, title = {A Non-Parametric Method for 3D Building Reconstruction Using Airborne Laser Scanner Point Clouds}, abstract ={In recent years, the techniques of surface representation have been changed and three-dimensional models have been replaced with two-dimensional maps. Airborne laser scanner as a powerful system has been known in remote sensing as a valuable source for 3D data acquisition from the Earth’s surface which can mainly be employed for 3D reconstruction and modeling. 3D reconstruction of buildings as an important element of 3D city models, based on LiDAR point clouds has been considered in this study. A new non-parametric method is proposed for generation of 3D model of buildings with flat and tilted roof. The approach comprises of four steps for 3D building reconstruction as: (A) Simultaneous building extraction and segmentation, (B) Edge detection, (C) Line approximation, and (D) 3D modeling. In step (A) a multi-agent method is proposed for extraction of buildings from LiDAR point clouds and segmentation of roof points at the same time. In this method five criteria such as height values, number of returned pulses, length, normal vector direction based on Constrained Delaunay Triangulation, and area are utilized. Next, in step (B) the edge points of roof segments are detected. Points of triangles having no neighboring triangles are extracted as primary edge points. In the extraction process, noises, external objects, and tree points on the roofs are eliminated. It is an advantage of the proposed method, however it leads to create the undesired edge points. There is the same problem regarding to segments which contain overlap with each other (like flat building). These undesired edge points as internal points are known and must be removed. In this paper, a method named “Grid Erosion” is employed for removing these internal points and therefore finding real edge points. After detecting the final edge points, a RANSAC-based algorithm is employed to approximate building lines in step (C). RANSAC is a powerful technique in line fitting and in comparison with general least square method, especially with noisy data, it provides robust results. In order to reduce the sensitivity of RANSAC to select parameters and no need for heavy post-processing, edge points are grouped by considering the angle between two consecutive connecting convex points. After classification of edge points, a RANSAC algorithm is separately applied on each classified edge-points group to produce primary lines. The regularization constraints should be applied on primary lines to generate the final lines. Finally, by modeling of the roofs and walls, 3D buildings model is reconstructed in step (D). The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. Building roof model is manually digitized from LiDAR point clouds and compared with building roof models that reconstructed using proposed method. In model reconstruction, the dominant errors are close to 30 cm which is calculated in horizontal distance. The main advantage of this method is its capability for segmentation and reconstruction of flat buildings containing parallel roof structures even with very small height differences (e.g. 10 cm). The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models.}, Keywords = {LiDAR, Point Clouds, 3D Model, Building Extraction, Segmentation, Line Approximation}, volume = {5}, Number = {2}, pages = {167-180}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-271-en.html}, eprint = {http://jgst.issgeac.ir/article-1-271-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Habibi, and M.R.Sahebi, and Y.Maghsoudi,}, title = {Object Based Ensemble Classifier for the Classification of Land Cover Polarimetric SAR Data}, abstract ={In this study, we used an object-oriented  method for merging pixel-based classification and image segments to get an optimal classification result. Urban land-cover classification is one of the important applications in polarimetric SAR remote-sensing images. Because of the nature of PolSAR images, many features can be extracted and used for classification. To achieve classification accuracy, optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with SVM as a pixel-based classifier with class accuracy as a criterion in feature selection.Also we  used  random feature selection for create multi-classifiers. In addition, because of speckle noise in PolSAR images, pixel-based classification result may not be satisfactory. Thematic features used in image segmentation can be helpful to solve this problem. In general, the proposed method has three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result is merged with a set of segments that are obtained from multi-resolution segmentation and the results are evaluated with overall accuracy and test pixels. The objectives of the study were to improve the accuracy of classification. Flowchart of the proposed algorithm presented as follows: The distinctive characteristic of synthetic aperture radar (SAR) sensors is the ability to provide a day-or-night, all-weather means of remote sensing. Recent SAR systems can produce high-resolution images of the land under the illumination of radar beams. SAR polarimetry is a technique that employs different polarization waves during transmission toward and reception from the Earth;#39s surface and the resultant PolSAR images can be used in identification of different classes based on analyzing different polarization backscattering coefficients; by assigning pixels into different classes using a classification technique, the information contained in the SAR/PolSAR images can be interpreted. Classifier ensembles or multiple classifier systems (MCS) are methods in pattern recognition that are used for image classification; by combining different independent classifiers, MCS can improve classification accuracy in comparison with a single classifier. There are different methods for creating such an ensemble. These methods include modifying the training samples (e.g. bagging [1] and boosting [2]), manipulating the input features (the input feature space is divided into multiple subspaces [3]), and manipulating the output classes (multi-class problem is decomposed into two multiple class problems, e.g. the error correcting output code [3]). After creating an ensemble of classifiers, a decision fusion is used to combine the outputs of the classifiers. Several fusion algorithms have been developed and employed in the literature like majority voting, fuzzy integral, weighted summation, consensus, mixed neural network, and hierarchical classifier system [4], [5]. Class-based feature selection (CBFS) is a method that chooses features for each class separately to create a multiple classifier with manipulating input features. We used this method for pixel-based classification, and then fused single classifiers in two different ways described in the next section. Experimental results showed that the overall accuracy of the proposed method (90.07%) has improved compared with the single SVM classifier and pixel-based multiple SVM classifiers (83.61%).}, Keywords = {Ensemble Classifier, Polarimetric Image, Object Based, Feature Selection, Class Based}, volume = {5}, Number = {2}, pages = {181-191}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-235-en.html}, eprint = {http://jgst.issgeac.ir/article-1-235-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {N.NeisanySamany, and M.R.Delavar, and M.R.Malek, and R.Aghataher,}, title = {Modeling Spatio-temporal relevancy in an Context-aware Geospatial Information System using Customized Fuzzy Multi-Interval Algebra}, abstract ={Ever increasing expansion of wireless and mobile technologies, electronic microprocessors and various communication tools have led to progress the Context-aware computing domain. Geospatial Information System (GIS) is one of the primary systems which utilize such technologies. The main challenge in context-aware GISs is the modeling of relevancy between the user and his/her related contexts. Space and time are two dominant factors to detect the relevant contexts and the other types of relevancies are dependent on spatial and temporal relevancies (spatio-temporal relevancy). Therefore, this research is concentrated on modeling spatio-temporal relevancy in context-aware GISs. It seems that there is no report on modeling all types of spatial relevancies (such as topological, metric and directional relationships) regarding the movement and cognitive characteristics of the moving user in urban context-aware GISs. Regarding this framework, the main contribution of the paper is that the proposed model applies customized Fuzzy Multi Interval Algebra and Voronoi-based Continuous Range Query to introduce spatio-temporal relevant contexts according to their arrangement in space based on the position, cognition, velocity and direction of the user in fuzzy approach. The customization process is undertaken based on the Comprehensive Calculus principles reducing 169 Allen’s relations to 25 spatio-temporal relations. In this research, the user is a tourist who is supposed to be guided from a hotel (his/her origin), to a defined destination. The context-aware system guides him/her according to spatio-temporal relevant contexts. The assessment of the implemented model is done regarding to three parameters including accuracy, time performance and users satisfaction. Tehran Districts 3, 6 and 11 are selected as the study area. In order to test the accuracy parameters, the designed software is run in three different routes with two different velocities and three time durations in 100 iterations. Then the accuracy of the detection of the related contexts is tested by means of a binomial distribution with one-sided 95% confidence level, precision and recall factors. The results of implementation and evolution demonstrated the efficiency of the proposed model in an urban context-aware wayfinding system.}, Keywords = {Spatio-temporal relevancy, Context-aware GIS, Dynamic Voronoi continuous Range Query, Allen’s Multi-Interval Algebra, Fuzzy Logic}, volume = {5}, Number = {2}, pages = {193-207}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-301-en.html}, eprint = {http://jgst.issgeac.ir/article-1-301-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {M.Davtalab, and M.R.Malek, and F.Naseri,}, title = {Providing Spatially Event-Condition-Action Policies in order to Resolve Policy Conflict in Ambient Intelligence Environment}, abstract ={Ambient intelligence environment contains a large number of devices, services and applications which govern specific tasks to assist end-users. Each service domain has itself policy and managing such environments needs policies to determine suitable behavior to certain events. Policies are the way of specifying and influencing management behavior within a system, without coding the behavior into the manager agents. Typically, policy-based management systems use some form of authorization and obligation policies to guide system behavior. Obligation  policies  specify  what  actions  an  entity  must  or must  not  perform  on  the  occurrence  of  an  event. Whereas, authorization policies specify  what  actions  an  entity  can  or cannot  perform  on  the  occurrence  of  an  event. Increasing ambient services may increase the number of users in joint areas of several domains simultaneously. These individuals will encounter unasked services which are sent by different domains in different policy patterns. In such conditions the role of policy conflict resolution and also service sending management is very important. Figure 1 shows two nested service domains with two different policies. The users locating in overlapped area are influenced by P1 and P2 services simultaneously. Consequently, conflicts will appear. P1 P2 ؟ Figure1: overlapped service domain Since a considerable percent of ambient services is allocated to the information services, so this paper focuses on these kinds of services which their aim is sending services to the users with the same priority. Thus it is possible to resolve the conflicts by adopting user context independent policies. The goal of this paper is to present spatially Event-Condition-Action (ECA) policies to resolve the conflicts of such users. These policies specify the actions to be performed when a certain event occurs and the specified condition is satisfied. Since each of users has itself contexts (i.e., location, speed, direction,..), so changes in these contexts leads to experience different spatial events. In this paper, spatial events including inside, entering/ leaving, accessibility and influenceability are considered. In our opinion, a spatial relation can be considered the equivalent of a spatial event. Consequently, spatial topology relations between the user and near service sites are surveyed. Finally according to the defined policies it will be decided which is the best composition of services for him/her?. In order to implement the proposed approach an area was considered consisting two nested domains. The first domain sends information services to the users within a 300-meter radius of a hospital. The second domain sends information including entertainment attraction, rides, accommodation of passengers and parking-place to the users within a 250-meter radius of a theme park which is located near the hospital. Then based on the event based policies, sent services to the users. The results show that 75 percent of the users were satisfied by the proposed services.}, Keywords = {Policy Conflict Resolution, Spatially Event-Condition-Action (ECA) Policies, Ambient Intelligence Environment}, volume = {5}, Number = {2}, pages = {209-217}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-233-en.html}, eprint = {http://jgst.issgeac.ir/article-1-233-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {D.Akbari, and A.Safari, and S.Khazai, and S.Homayouni,}, title = {Improved Spectral-Spatial Classification Minimum Spanning Forest by Reducing the Spatial Dimensions of Hyperspectral Images}, abstract ={Imaging spectroscopy, also known as hyperspectral imaging, is concerned with the measurement, analysis, and interpretation of spectra acquired from either a given scene or a specific object at a short, medium, or long distance by a satellite sensor over the visible to infrared and sometime thermal spectral regions. The recent developments in spatial, spectral and radiometric resolution of hyperspectral images have stimulated new methodologies for land cover and land use classification. There are two major approaches for classification of hyperspectral images:  the spectral or pixel-based and the spectral-spatial or object-based approaches. While the pixel-based techniques use only the spectral information of the pixels, the spectral-spatial frameworks employ both spectral characteristics and spatial context of the pixels. The pixel-based classification methods are often unable to accurately differentiate between some classes with high spectral similarity. This is mainly because they employ only the spectral information in order to identify different land covers. Consequently, methods that can exploit the spatial information are essential for more accurate classification results. Among the various methods for extracting spatial information, segmentation techniques are the powerful tools for defining the spatial dependences among the pixels and finding the homogeneous regions in the image. An alternative way to achieve the accurate segmentations of image is marker-controlled segmentation. The idea behind this approach is selecting of one or several pixels for every spatial object as the seed or a marker of the corresponding region. The marker-based segmentation significantly reduced the over-segmentation problem and led to better accuracy rate. Recently, an effective approach to spectral-spatial classification of hyperspectral images has been proposed based on Minimum Spanning Forest (MSF) grown from automatically selected markers using Support Vector Machines (SVM) classification. In this framework, a connected components labelling is applied on the classification map. Then, if a region is large enough, its marker is determined as the P% of pixels within this region with the highest probability estimates. Otherwise, it should lead to a marker only if it is very reliable. A potential marker is formed by pixels with estimated probability higher than a defined threshold. This paper aims at improving this approach by reducing the spatial dimensions of hyperspectral images. The proposed approach are evaluated the dimension reduction of hyperspectral image before and after marker selection process in MSF using genetic algorithm. The genetic algorithm is a general adaptive optimization search method based on a direct analogy to Darwinian natural selection and genetics in biological systems. It starts from an initial population which is composed of a set of possible solutions called individuals (chromosomes), and then evaluates the quality of each individual based on a fitness function. We use the Kappa coefficient accuracy parameter of SVM classification obtained from the training samples subset as the fitness function. Three benchmark hyperspectral datasets are used for evaluation:  the Pavia dataset, the Telops dataset and the Indian Pines dataset. Experimental results show the superiority of using genetic algorithm before selecting markers in Pavia and Telops datasets. In Indian Pines dataset, the classification accuracy was increased with reduced dimensions both before and after the marker selection and concurrently.}, Keywords = {Hyperspectral Image, Spectral-Spatial Classification, Marker-Based Minimum Spanning Forest Algorithm, Genetic Algorithm}, volume = {5}, Number = {2}, pages = {219-229}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-108-en.html}, eprint = {http://jgst.issgeac.ir/article-1-108-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {D.P.Rad, and A.R.Vafaeinejad,}, title = {Using a GIS Based Decision Support System to Aid Earthquake Crisis Management with Site Selection of Temporary Housing Case Study: District 8 of Isfahan Municipality}, abstract ={Proper Site Selection of effective buildings in rescue (temporary housing) is necessary for long-term welfare of earthquake affected people. To select a suitable for the temporary accommodation of the population affected by natural disasters have always been considered and organization responsible for managing the crisis. Find a place to help citizens usually occurs by temporary help agencies without regard to standards and according to the personal experiences after an accident. Its clear that to choose an inappropriate place to house survivors is worse than the first place. This research proposes a model for appropriate and systematic site selection for temporary shelters, before an earthquake, using a Multi Criteria Decision Making (MADM) and Geographical Information System (GIS). A GIS-based system uses electronic mapping technology in producing interactive multi-layer maps so that queries are set to find optimal solutions for problems. It combines spatial and non-spatial data to construct visualized information that can be easily analyzed by decision makers. In this research, The district 8 of Isfahan municipality have been selected and studied based on three criteria; Population, aging tissue and culture. In this study we identify effective measures to locate temporary housing that has been selected from information relevant to the investigation according to the study area and data accessible. We categorized all the mentioned criteria in five main categories. Safety, The suitability of the land, Community and neighborhood, Availability and population are the main categories. A total of twenty-eight were considered criteria for selecting the suitable location. Based on the analytical methodology after using the criteria, weight matrix of paired comparisons the expert opinions have been obtained. Care centers and the sale of fuel, with the weight of 15.2%, is the most important criterion in this analysis. The remaining criteria have a weight range from 2% to 9.7%. Data associated with each of the layers by the appropriate phase, were fuzzy. Using hierarchical analysis and ArcGIS software, production levels for each criterion according to the specified weight, joined together, the output of the zoning map of the study area is temporary housing for injured owes. After that the value of 13 parks as a place for temporary housing calculated. In addition to the analytic hierarchy process for temporary housing location were performed using Vikor and Topsis. According to the ranking of the different ways, that the arithmetic mean was the first of these Borda and Copeland were then re-rating methods and ultimately to obtain the final ranking of the arithmetic mean of the three methods were different. Based on the analysis of the results of the comparison methods was determined that Vikor was the best and the closest final ranking and Topsis and AHP methods were in the next places. According to the final ranking Golestan park, North Noosh park and South Noosh park are the best places in temporary housing in the district. In addition, a parish map of the district was prepared. The map shows that the parishs of Bahramabad, Kesareh and Ferdovan respectively are the top ranking.}, Keywords = {Temporary Housing, Crisis Management, Site Selection, GIS Based Decision Support System, Analytical Hierarchy Process, Topsis, Vikor}, volume = {5}, Number = {2}, pages = {231-246}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-374-en.html}, eprint = {http://jgst.issgeac.ir/article-1-374-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {Z.ZakaryaieNejad, and A.Ezzatpanah, and R.Ramezanian,}, title = {Using an Imperialistic Competitive Algorithm in Global Polynomials Optimization (Case Study: 2D Geometric Correction of IKONOS and SPOT Imagery)}, abstract ={The number of high resolution space imageries in photogrammetry and remote sensing society is growing fast. Although these images provide rich data, the lack of sensor calibration information and ephemeris data does not allow the users to apply precise physical models to establish the functional relationship between image space and object space. As an alternative solution, some generalized models such as global polynomials have been developed and used. This paper presents a hybrid method based on using imperialistic competitive algorithm (ICA) to find the best terms of global polynomials. The method was carried out for geometric correction of two different datasets, an IKONOS Geo-image and a SPOT image, with different number of ground control points (GCPs) and independent check points (ICPs). Results showed the success of achieving sub-pixel accuracies (0.2) for IKONOS and 2.5 pixels for SPOT image. The method was able to successfully handle over-optimization as it produces lower RMSEs compared to conventional approach. Also, the proposed method required much less time in comparison to other optimization algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).  }, Keywords = {Geometric correction, Global polynomials, Best terms selection, Imperialistic competitive algorithm}, volume = {5}, Number = {2}, pages = {250-257}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-153-en.html}, eprint = {http://jgst.issgeac.ir/article-1-153-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.R.Amiri-Simkooei, and H.Ansari, and M.A.Sharifi,}, title = {Application of Recursive Least Squares to Efficient Blunder Detection in Linear Models}, abstract ={In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is the detection and elimination of outliers, which may appear to be inconsistent with the remainder of the observations or the model. Outlier detection is thus a primary step in many geodetic applications. There are various methods in handling the outlying observations among which a sequential data snooping procedure, known as Detection, Identification and Adaptation (DIA) algorithm, is employed in the present contribution. An efficient data snooping procedure is based on the Baarda’s theory in which blunders are detected element-wise and the model is adopted in an iterative manner. This method may become computationally expensive when there exists a large number of blunders in the observations. An attempt is made to optimize this commonly used method for outlier detection. The optimization is performed to improve the computational time and complexity of the conventional method. An equivalent formulation is thus presented in order to simplify the elimination of outliers from an estimation set-up in a linear model. The method becomes more efficient when there is a large number of model parameters involved in the inversion. In the conventional method this leads to a large normal matrix to be inverted in a consecutive manner. Based on the recursive least squares method, the normal matrix inversion is avoided in the presented algorithm. The accuracy and performance of the proposed formulation is validated based on the results of two real data sets. The application of this formulation has no numerical impact on the final result and it is identical to the conventional outlier elimination. The method is also tested in a simulation case to investigate the accuracy of the outlier detection method in critical cases when large amount of the data is contaminated. In the application considered, it is shown that the proposed algorithm is faster than the conventional method by at least a factor of 3. The method becomes faster when the number of observations and parameters increases. }, Keywords = {Outlier detection, Baarda data-snooping method, DIA algorithm, w-test statistic, Hypothesis testing, Geodetic network analysis}, volume = {5}, Number = {2}, pages = {258-267}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-154-en.html}, eprint = {http://jgst.issgeac.ir/article-1-154-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2015} } @article{ author = {A.Aghabalaei, and H.Ebadi, and Y.Maghsoudi,}, title = {Forest Cover Classification Using Compact Polarimetry Data}, abstract ={Indeed, Full Polarimetry (FP) imaging system has proven its increased potential in various applications, but suffer from an increase in the Pulse Repetition Frequency (PRF) and the data rate over single polarization. Recently, there has been growing interest in Dual Polarimetry (DP) imaging system that is called Compact Polarimetry (CP). The CP is a new mode proposed in DP system which has several important advantages in comparison with the FP mode such as reduction ability in complexity, cost, mass, and data rate of a Synthetic Aperture RADAR (SAR) system. Moreover, this new mode not only can achieve a greater amount of information than standard DP modes, but also can cover a much greater swath widths compared to FP mode. For this reason, the CP data can be critical for monitoring applications, such as forest controlling and monitoring. Forest studying is the one of research areas that is attractive for RADAR remote sensing researchers, because it has an effective role in climate controlling. Therefore, forest cover classification is essential to manage natural resources and environment, land use plans and land potential. Despite the significant number of works carried out for CP SAR applications, very few researches have been performed to investigate the capability of CP data for forest cover classification. In this paper, the potential of CP data in forest area is investigated using complex Wishart classifier in two ways. First, we use 2×2 covariance matrices of the pi/4 and Circular Transmit-Linear Receive (CTLR) CP modes simulated by RADARSAT-2 FP mode which acquired over Petawawa research forest and second, 3×3 covariance matrices reconstructed from these modes were exploited. Next, we compare the results with the FP mode. Results of this study show that the pi/4 mode provide better overall accuracy in forest cover classification than the CTLR mode, as well as extending the CTLR mode via Souyris’s iteration model to generate the PQ_CTLR mode overally does not significantly affect the Wishart classification. However, construction of the PQ mode permits a direct comparison of the average scattering mechanisms. Therefore, in the next step, the Cloude-Pottier alpha angle is considered and calculated for PQ and FP modes. The PQ_pi/4 mode shows that it is a better mode to estimate the alpha angle parameter compared to the PQ_CTLR mode. This study shows that although CP modes do not produce as good classification accuracies as produced by FP mode, they are an effective strategy when the polarimetric system resources are limited or not available. Also, they are compatible as an optional mode for a FP SAR system. Moreover, circular polarizations e.g. CTLR modes, will be less sensitive to Faraday rotation effects attached to low frequency propagation in the ionosphere. At the present, at least two earth observation satellites which provide CP modes are already in orbit or to be launched in next few years. These are Indian RISAT-1, Japanese Advanced Land Observing Satellite-2 (ALOS-2), Canadian RADARSAT Constellation Mission (RCM) and Argentina SAR Observation & Communications Satellite (SAOCOM) that will able to collect the CP data in  and CTLR modes.}, Keywords = {Compact Polarimetry, Forest Cover Classification, Complex Wishart Classifier}, volume = {5}, Number = {3}, pages = {1-14}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-258-en.html}, eprint = {http://jgst.issgeac.ir/article-1-258-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {R.Shah-Hoseini, and A.Safari, and S.Homayouni,}, title = {An Automated Kernel-based Change Detection Method in Urban Area Using Landsat Multispectral Images, Case Study: City of Karaj}, abstract ={In the past few decades as a result of urban population, spatial development of urban areas has been growing fast. This has led to some changes in the environment in these areas. Hence, detecting changes in different time periods in urban areas has a great importance. Conventional CD methods partition the observation space linearly or rely on a linear combination of the multitemporal data. As a result, they can be inefficient for images corrupted by either noise or radiometric differences that cannot be normalized. On the other hand, one of the main challenges in the production of maps of changes in urban areas, Constraints on the spectral separation of bare land and built-up area from each other in these areas. Therefore, in this paper, an automatic kernel based change detection method with the ability to use a combination of spectral data and spectral indices have been proposed. First, the spectral index for the separation of classes covering the urban area of multi-temporal images are extracted. In next step, differential image was generated via two approaches in high dimensional Hilbert space. By using change vector analysis and determining automatically a threshold, the pseudo training samples of the change and no-change classes were extracted. These training samples were used for determining the initial value of kernel C-means clustering parameters. Then, an optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the kernel based C-means clustering’s parameters and to select the precise training samples. These training samples were used to train the kernel based minimum distance (KBMD) classifier. Lastly, the class’s label of each unknown pixel was determined using the KBMD classifier. To assess the accuracy and efficiency of the proposed change detection algorithm, this algorithm were applied on multi-spectral and multi-temporal Landsat 5 TM images of the city of Karaj in 1987 and 2011. Respect to the features used, the sensitivity analysis for proposed method carried out using five different feature sets. In order to assess the performance of the proposed automatic kernel-based CD algorithm in the case of using DFSS (Accuracy: 86.40 and Kappa: 0.83) and DFHS (Accuracy: 85.54 and Kappa: 0.82) differencing methods, we compared this technique with well-known CD methods, namely, the MNF based (Minimum Noise Fraction) CD method (Accuracy: 77.42 and Kappa: 0.76), SAM (Spectral Angle Mapper) CD method (Accuracy: 64.60 and Kappa: 0.60), and simple Image differencing CD method (Accuracy: 73.44 and Kappa: 0.70). The comparative analysis of proposed method and the classical CD techniques show that the accuracy of obtained change map can be considerably improved.}, Keywords = {Spectral Indices, Automated Kernel-based Method, Change Map, Clustering Algorithm, Pseudo Training Samples, One-class Classification, Cost Function, Optimization}, volume = {5}, Number = {3}, pages = {15-34}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-297-en.html}, eprint = {http://jgst.issgeac.ir/article-1-297-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Hasanlou, and P.Pahlavani, and H.Amini, and S.TalebiNahr,}, title = {Feasibility Study of Detection Specific Urban Features Using High Spectral Resolution Satellite Imagery, LIDAR Data, and Fully Polarimetric RADAR Imagery}, abstract ={The urban area has always been under the influence of population growth and human activities. This process causes the changes in land use/cover. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. On the otherhand, satellite remote sensing has several advantages for monitoring land use/cover resources, especially for urban areas. In this regards, classifiying urban area over time present additional challenges for correctly analyzing remote sensing imagery. Nowadays, integrating different kinds of data and images, achieved by the different remote sensing sensors are known as a suitable solution for extracting more useful information. The passive optical sensors have been used extensively in mapping horizontal structures. However, radar data could be used as a complementary data, since these data would be gathered in different climatic conditions in 24 hours of a day, as well as some geo and manmade structures have a specific response in the radar frequency. Furthermore, LiDAR data could gather precise measurements from vertical structures. Hence, by integrating optical, radar, and LiDAR data more features and information would be prepared for different kinds of applications. In this research, we used these data sets to detect buildings, roads, and trees in a complex city sense, i.e., San Francisco, by generating 141 features, in passive optical sensors using high spatial resolution WorldView-2 imagery (image bands, vegetation indices, IHS color space, YIQ color space, YCbCr color space, the first order statistical features and the second order statistical features), also in LiDAR data (first and last pulse, first and last intensity, nDSM, NDI, slope 4 neighbor, slope 8 neighbor, aspect, roughness, smoothness, surface curve, profile curve, variance and laplacian) and in RADAR data using RADARSAT-2 (amplitude, phase, intensity, incidence angle, imagery part, real part, radar cross section, polarized HH, polarized VH, polarized HV, polarized VV, ratio element of scattering matrix, alpha, beta, pauli coefficient, krogager decomposition coefficient, freeman decomposition coefficient, yamaguchi decomposition coefficient, antropy, eigen value and anisotropy). We divide our merging data set to four regions. The first region include building feature, the second region include building and vegetation features, the thid region include building and road features and the forth region include vegetation feature. All thsese features merged and produc the cube of data with 141 dimension number. Then, by using the principal component analysis (PCA) feature extraction method, as well as the well-known intrinsic dimension (ID) methods, including second moment linear (SML) and noise whitened HFC (NWHFC) dimensionality of these data sets is reduced. Finally, the supervised classification method k-nearest neighbour (K-NN classifier) was utilized in order to detect buildings, roads, and trees and grouping features according to the earned accuracies. In this regards, the thirty present of ground truth data was used as traning data sets and remaing seventy present as test data sets. In addition, the fusing and merging these data sets (buildings, roads, and trees) reveal the superiority of the implemented method to classify map with overall accuracy by a margin of nearly 90% using proposed approach and support our analyses.}, Keywords = {Image Classification, RADAR, LiDAR, Optic, Feature Detection}, volume = {5}, Number = {3}, pages = {35-48}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-209-en.html}, eprint = {http://jgst.issgeac.ir/article-1-209-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {B.Vahedi, and A.A.Alesheikh,}, title = {Assessing the Attribute Accuracy of Volunteered Geographic Information}, abstract ={Since the emergence of the concept of Volunteered Geographic Information (VGI), the quality of this type of information is presented as its biggest problem. Therefore, this issue has been addressed frequently in the literature, and scientists have tried to evaluate the quality of VGI. However, attribute accuracy, despite its important role in a variety of spatial analyses and applications of VGI, has received less attention in comparison to other elements of quality in the literature. Positional accuracy, completeness, lineage, resolution, and time accuracy are among the most important elements of spatial data quality.  In this study, using a novel method and by leveraging Levenshtein algorithm along with text pre-processing, attribute accuracy of volunteered geographic features is examined, comparing this data with reference data. Levenshtein algorithm calculates the difference between two strings of text by counting the number of changes (edits) necessary to change one word to another, and thus sometimes is referred to as Levenshtein distance. The first step of the proposed method is to find corresponding features in the two data sets to perform the comparison based on. This step is done by applying an automatic data matching algorithm between the two sets. This algorithm consists of five stages, each applied on either the reference data set or the VGI data set. After data matching is done, each VGI feature is compared with its corresponding match in the reference data set and the Levenshtein distance between the “name” attribute of these two features is calculated. Then, features are categorized as having correct (accurate), approximately correct, or incorrect names based on the Levenshtein distance and assuming that the name of the reference features are correct. For VGI features without a match in the reference data set, a search distance is defined, inside which reference features with the exact same name as the VGI feature are sought. The study area of this research is Tehran city, Iran. A data set produced by the municipality of Tehran is used as the reference data set and OpenStreetMap data as the VGI data set. According to the results, 47 percent of VGI features have a name attribute and among these, 33 percent of them have correct name, 44 percent have approximately correct name, and the remaining 23 percent have incorrect names. The Overall attribute accuracy of the VGI data set used in this study, is thus 77 percent, indicating that among those features that have a name attribute, 77 percent of them have either correct or approximately correct names. A future line of research, based on the findings of this paper, could be to develop methods for evaluating the attribute accuracy of a data set without having to compare it with a reference data set.}, Keywords = {Volunteered Geographic Information, attribute accuracy, Levenshtein algorithm, spatial data quality, data matching, OpenStreetMap}, volume = {5}, Number = {3}, pages = {49-64}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-348-en.html}, eprint = {http://jgst.issgeac.ir/article-1-348-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {A.Madadi, and F.Karimipour,}, title = {Temporal Modeling of Geographically Weighted Regression for Extraction of Relationships between Land Use/Land Cover and Water Hardness}, abstract ={The effects of human activities on the surface water quality have been exhaustively investigated. The results have shown that the impact of land use/land cover on the surface water quality varies from a location to another. However, the temporal characteristics of the problem are poorly understood. This paper examines the hypothesis states that the impact of land use /land cover on the surface water quality varies with time i.e., it is a spatio-temporal linkage. Spatio-temporal data mining analyses were deployed, as they are capable of extracting novel patterns and correlations hidden in the data. Water quality parameter including As (Arsenic) was considered for 12 water quality monitoring stations across Seattle, Washington. These parameters were examined against five land use/land cover types (urban, cultivation, hay, forest, and wetland) for 9 years from 1998 to 2006 to study how land use/land cover influence the parameters. Due to non-stationary intrinsic of the problem as well as spatial auto-correlation exist between the values observed at the monitoring stations; the ordinary least square regression produces unwanted bios in the results. Hence, the geographical weighted regression (GWR) was used in order to model the spatially varying characteristics of the problem. Furthermore, to incorporate the time-varying effects, the model was calibrated for values collected at the stations separately for wet and dry seasons. The linkage between LULC and the amount of Arsenic (as the case water quality parameter) at each station was extracted using the temporal geographically weighted regression (Equation 6 at paper) separately for the years 1998 to 2006. Figures 6 and 7 (at paper), respectively, show the results for the wet and dry seasons of the year 2006, classified based on the residual square ( ), which is a measure of goodness of fitness. Larger values for  indicate more linkage between the LULC class and the water quality parameter. (Note: because of the limitation in space, the results for other years are not shown). We also applied a significance test on the extracted linkage at the confidence level of 95%. The results illustrate that except for cultivate lands, other classes could reasonably show the spatial variety of the linkage. On the other hand, comparing the results of the wet and dry seasons shows that the model could extract the linkage in wet seasons more efficiently than dry seasons. For example, while there is a negative relation between the urban land use and the amount of Arsenic for wet seasons at most of the stations, there is no significant relation between them at dry seasons. The reason could be the effect of seasonal changes on the water quality parameters due to seasonal rainfalls. On the other hand, there is a positive linkage between forests and the amount of Arsenic for both of wet and dry seasons. However, this linkage completely follows different patterns for wet and dry seasons, which could possibly happen because of the land cover change in the wet and dry seasons. These two examples certify that seasonal changes have considerable effects of the linkage between LULC and water quality parameters, and so must be treated separately.}, Keywords = {Soatio-Temporal Autocorrelation , Spatio-Temporal Nonstationarity , Water Hardness , Seasonal Index , Geographically Weighted Regression}, volume = {5}, Number = {3}, pages = {65-76}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-215-en.html}, eprint = {http://jgst.issgeac.ir/article-1-215-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {R.Foroozandeh, and F.Hakimpour, and N.Khademi,}, title = {Short Term Prediction of Travel Time on Links of Network using Transit Buses Positioning System Data}, abstract ={Link travel time is the most important variable for determining travel route and start time of journeys. Link travel time is the basis of navigation and routing systems. Time dependent algorithms in Geographic Information Systems (GISs) calculate fastest routs based on the travel duration of links of a network depending on different times or dates. In fact, travel time estimation for future is the basis of time dependent routing. Currently different means of monitoring traffic flow such as traffic cameras or electromagnetic sensors are present [1]. However, these methods cannot estimate link travel time efficiently; high cost, low accuracy and dependency on human agents are major problems of using these methods. By emerging usage of portable receivers of positioning systems, researchers are more interested in exploiting the data produced by such equipments for monitoring traffic speed flow. Nowadays most public transit buses are equipped by AVL systems for monitoring purposes. In this research, travel durations of arterial links are estimated in real time by obtaining data from transit buses. Essential corrections for complications caused by buses slower speed and their leaving traffic flow at bus stops are modeled and applied towards a better assessment [5, 6]. Determining link travel durations in time intervals needs an analysis on spatio-temporal data. We estimate travel duration for time intervals of 15 minutes length (7am to 9pm) assuming invariant parameters in calculation of travel duration for each interval. In our approach, first we calculate the travel duration for buses and compensate for the delays caused by bus stops, which include the acceleration and deceleration time at each stop. Simultaneously, timing data of traffic lights control signals are also incorporated in computations to improve accuracy of bus travel duration estimation. We use historical data to find required parameters for calculating bus travel duration. In addition to historical data, we also integrate real-time data and time series analysis to improve our travel duration estimation. In this research we use Holt-winters analysis [14] for a short term prediction of travel time. Finally, we obtain a set of observation equation that is solved by an optimization method. Buses movement data of two different bus routes in five days (6th to 10th December 2014) are used to estimate travel duration of three links in Motahari Street. Position information of each bus is provided every two minutes plus the time and position of every time the bus doors opens and closes. On the fifth day (10th December) three test vehicles equipped with GPS recievers are employed to collect validation movement data every one second. Drivers of the test vehicles are instructed to avoid extreme low or hight speed and drive with the flow of traffic in the middle lanes insofar as possible. Finally, calculated travel times are compared with results of two well-known methods namely baseline estimation algorithm [8] and Helinga method [4]. RMSE of the proposed approach indicates 22 percent improvement compared to Helinga approach and 30 percent improvement in comparison to baseline algorithm. This improvement shows that information obtained from a public bus monitoring system can be used efficiently for arterial link travel time estimation.}, Keywords = {Link Travel Time, Bus AVL, Holt-Winters Analysis, Real Time Calculation, Spatio-Temporal Data}, volume = {5}, Number = {3}, pages = {77-98}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-342-en.html}, eprint = {http://jgst.issgeac.ir/article-1-342-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {B.Jamali, and A.Sadeghi-Niaraki,}, title = {Improving Geo-labeling in Ubiquitous Environment Based on Augmented Reality}, abstract ={During the recent years, smartphones have substantially evolved in terms of software and hardware technological properties. In addition, the ubiquity feature of these devices has increased their usage in ubiquitous geographic information systems (UBI-GIS), especially in case of data visualization. Accessibility of modern mobile phones allows the users to overcome limits of time and space. This property provides a possibility to achieve the most important element of ubiquitous computing. In fact, smart phones combine potentials of various tracking techniques such as GPS and IMU sensors with high-resolution cameras that allow data visualizations anytime and anywhere. In ubiquitous computing, augmented reality (AR) has been discussed as a ubiquitous user interface to the real world and AR technique is considered as a user interface in ubiquitous computing systems. AR is an excellent user interface for mobile computing applications, because it allows intuitive information browsing of location-referenced information. In an AR environment, the user’s perception of the real world is enhanced by computer-generated entities such as 3D objects and spatialized audio. An AR system requires at least three significant components: a model of the environment in which the system should be deployed, a real-time capable method for tracking the human user and an ergonomically acceptable mobile hardware setup. In this paper, these three core topic has been selected in following way: 3d model of area in 3D GIS as model of the environment, MEMS inertial sensors, MEMS compass and GPS sensors as tracking hardware and smartphone as mobile hardware setup.  There are many methods for data visualization in AR systems. Geo-labeling is one method for data visualization in ubiquitous computing, which is tightly connected to Geospatial Information Systems(GISs). GIS can play an important role in improving AR systems. GIS can be exploited for the creation of content and geospatial analyses such as visibility analysis can be used to improve AR systems.  QR code and RFID are two traditional methods for Geo-labeling and data retrieving that require special equipment and large budget. Other method to identify surrounding environment is georeferenced objects that can be retrieved by their location. In this approach, AR view show many content because of there is no filtering method.  In this paper, the proposed system integrates innovative technologies of 3D GIS with tools for 3D visibility and AR view in order to establish an improved Geo-labeling system. The principal components of the proposed system are explained and smart phones are employed as the platform for creating a ubiquitous environment, which conducts the Geo-labeling process.  Haft-e Tir square in the Tehran City is selected for implementing the proposed method and an Android’s application is developed as well. Quantitative assessment of the system results indicates high performance of the AR technology for data visualization with high accuracy and applicability in an urban environment. The results indicate geo-labels are linked to their corresponding objects with an acceptable accuracy. Finally, the efficiency of system for geo-labeling and AR-based data visualization are analyzed through quantitative and quantitative methods, which supports development of systems with improved functionality.}, Keywords = {Ubicomp, Visualization, Smartphone, Geo-labeling, Augmented Reality}, volume = {5}, Number = {3}, pages = {99-110}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-292-en.html}, eprint = {http://jgst.issgeac.ir/article-1-292-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {F.Saeedzadeh, and M.R.Sahebi, and H.Ebadi, and V.Sadeghi,}, title = {Change Detection of Multitemporal Sattelite Images by Comparison of Binary Mask and Most Classification Comparison Methods}, abstract ={Map production and usable information in Geospatial information system have notable cost and time allocated that finally such information and decisions are based further activities, especially in urban areas. Updating data involves the development of a geospatial information system and use of it. Change detection process, provides context for updating information and the latest applications and challenging in many branches include: urban planning, the environment and other sciences of the earth. Common techniques that used to Change detection, usually are based on pixels. In this study, two binary mask and post classification comparison method was used in combination and then compared the results of the comparative method of classification. Binary mask is a combination of Fuzzy thresholding and Automatic thresholding method such Otsu, Then comparing the classifiers such as, maximum likelihood, support vector machines, nearest neighbor and neural networks were used. The data set is provided by a couple of acquired very high resolution images on the Azadshahr region, District 22 of Tehran city (Iran) by the Quickbird and GeoEye sensors in 2006 and 2011 respectively. This data set is characterized by 3 visible spectral bands (Blue, Green and Red). The results show that the proposed method in terms of qualitative and quantitative comparison showing the changes against the post classification comparison method was more accurate and the overall accuracy and Kappa coefficient by using neural networks to map the changes resulting from this method is the equivalent of 73.32 and 68.38. However, the accuracy of the post classification comparison for neural networks of 65.61 and 48.96 against the kappa coefficient is obtained.}, Keywords = {Pixel Based Change Detection,Automatic Otsu Thresholding,The Binary Mask}, volume = {5}, Number = {3}, pages = {111-128}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-304-en.html}, eprint = {http://jgst.issgeac.ir/article-1-304-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Goli,}, title = {Impact of Gridding on Stability of Downward Continuation of Gravity Anomaly}, abstract ={The solving of third geodetic boundary value problem need to gravity anomalies continued from surface of the Earth down to their mean values on geoid. Downward continuation (DC) is the most challenging part of precise geoid determination. The inverse of Poisson’s integral are frequently used by researchers for DC.  In this paper, the planar approximation of Poisson’s integral is used which provides the same accuracy respect to other higher approximations such as, spherical or ellipsoidal. The DC problem is inherently ill-posed being highly sensitive to high frequencies part of gravity signal. The DC process is ill-posed in its continuous mode. For the numerical evaluation, the process as a linear system (Ax=b) will be well-posed if the Hadamard conditions: existence of solution, uniqueness and stabilities are fulfilled. The existence and uniqueness of solution is guaranteed physically, but the process may be unstable, i.e., the solution does not grasp continuously on the data (b). The continuous problems must be discretized in order to prepare for a numerical evaluation. The discretization form of an ill-posed problem may turn to a well-posed depending on the discretization step. In DC process, the spacing of gravity anomalies is a major factor for conception of instability. The discretization of Poisson’s integral equation can be done using two different mean (grid) and point (scatter) schemes. Usually, the gravity data are observed at scattered point such as at leveling benchmarks. Then, the mean gravity anomalies are predicted/averaged on regular mesh. DC of gridded gravity anomalies are much easier to implement and more stable due to the attenuating of the high frequency by averaging. In addition, the stability of linear equation systems is increased by removing the very close observations. However, the useful local part of gravity signal are lost by averaging and mean anomalies are unavoidably affected by perdition error particularly in regions of poor data coverage. The mean gravity anomalies on geoid can be directly computed from DC of observed gravity anomalies. This process leads to ill-condition linear system in most cases. Hence the some appropriate regularization methods need to obtained the desired accuracy. The DC of scattered data has some advantages such as, there is not prediction error in them or they contain all frequencies of gravity filed. In this study, the accuracy and stability of DC of scattered and gridded anomaly are investigated. The discrete Picard condition is utilized to study the ill-poseness and instability of the DC linear equations system. Numerical examination is done in two mountainous test areas in Iran with a poor gravity data coverage and in the USA with dense gravity observations. Numerical results in both test areas show that the DC of scattered anomalies is an ill-posed due to closeness of point anomalies in some areas such as along levelling lines. Whereas the DC of 5;#39×5;#39 gridded anomaly is a well-posed and stable problem. The DC of EGM08 synthetic gravity anomalies indicates that despite the presence of prediction error in gridded anomalies and the removing some useful high frequencies, their results are more accurate than scattered anomalies.}, Keywords = {Downward Continuation, Ill-posed Problems, Instability, Gridding, Gravity Anomaly}, volume = {5}, Number = {3}, pages = {129-138}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-343-en.html}, eprint = {http://jgst.issgeac.ir/article-1-343-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.R.Mobasheri, and E.Amraei,}, title = {Correction of Vertical Noise Stripes in Images Acquired by CCD-Camera on Board of CBERS02 Satellite}, abstract ={CCD Camera is a multi-spectral sensor placed on CBERS02 satellite platform. The imaging technique in this sensor is pushbroom. There are some vertical noises in the images captured by this sensor all because of unadjustment between different adjacent detectors in the CCD-Camera sensor, internal changes in detectors, mis-calibration and low values of signal to noise ratios. These noises for homogeneous surfaces in level2 products are more profound. The presence of these noises in the images renders correct interpretation and extraction of information hard. In this study, for correction of noise a method based on the spatial momentum adjustment is introduced. In the proposed method the statistical momentums such as mean and standard deviation of the column in each band are used for stabilization of the statistical characteristics of the detector array to their reference values. In the simulated image for vertical noise, 97% accuracy in denoising was achieved. Moreover 16% increase of the image quality in band1 and 19% in band2 shows the acceptable performance of the method in denoising. Also by implementing the method on band1 and 2 images, the standard deviation decreased from 9.47 to 9.01 and 5.72 to 5.25 respectively. This proves that the method was a success.}, Keywords = {CCD Camera, CBERS, Striping Noise, Satellite Image, Remote Sensing}, volume = {5}, Number = {3}, pages = {139-150}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-221-en.html}, eprint = {http://jgst.issgeac.ir/article-1-221-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.J.ShahidiNejad, and A.A.Alesheikh, and A.KalantariOskoei,}, title = {Assessment of National Spatial Data Infrastructure Using BSC Model}, abstract ={An important issue in research on spatial science is the development and uses of a suitable framework for implementation, assessment and continuous improvement of Spatial Data Infrastructure (SDI). Due to the development of a National SDI in Iran, the need to justify the used resourced and to verify the efficiency of the implemented NSDI are felt. Creating SDI at global, regional, national, state, local and organizational level is a challenging issue for sustainable development of states. SDI assessment can be used to check whether the results of implementing SDI are effective; it can also increase common knowledge of SDI to improve future performances. The main objective of this study is to evaluate the Iranian SDI from different views. This research seeks to answer the question of how to evaluate the performance of an SDI at national level. The detection of strengths and weaknesses of the implemented SDI is another objective of this paper. For this purpose, various models for performance assessment were studied, and an appropriate one is selected. The selected model were modified to match the NSDI criteria. In this research, the Balanced ScoreCard (BSC) was selected for NSDI assessment. BSC assesses the performance from different perspectives, including financial, customer, internal processes and learning and growth perspectives. In order to fulfill the requirements, different views were considered. The most important points for an NSDI assessment include the financial metrics, strategies used, available technologies, users, employees, management, human resources, and the policy practiced. Through adoption of these criteria to the BSC perspectives, an appropriate method for NSDI performance assessment is proposed. Stability of an SDI assessment framework from different points of views is based on multiplicity of assessment views. The benefit of this method is the flexibility of the framework that permits to increase new perspectives of assessment and the adjustment and removal of the ones that exist. The evaluating of the Iranian NSDI using BSC model was performed for the first time in the country.  In order to achieve the objectives of the study, various steps were taken. First, different views were considered. A questionnaire containing 62 questions with 5 options were prepared to include all aspects of NSDI assessment. After determining the sample size with visiting the centers and organizations in the field of Geomatics, the questionnaires were completed by experts in this field. The reliability test was done to ensure whether the questionnaire is able to assess the NSDI. Then, the weight for each question was calculated. Following, each questionnaire was weighted based on: the degree and the field of study; the work experience in the field of Geomatics, the organization of the person who filled the questionnaire and the correlation between answers for each person who responded. Then weights are normalized. After adopting the SDI assessment criteria with BSC method, criteria, sub-criteria and weights for each sub-criteria were estimated. The final results of each of the four perspectives of BSC model showed that financial perspective has a 34.56%, the customer perspective has a 41.08%, the internal processes has a 49.67% and learning and growth perspective has a 39.75% performance ratio. The results showed that the current situation of NSDI in Iran is not satisfactory. To improve this condition, more efforts are needed in all perspectives. Most of the weaknesses were seen in the financial field; while in the field of internal processes performance was better in comparison to others. Regarding all four perspectives, performance value of NSDI in Iran was estimated as 41.27% which is lower than the average yield. Therefore; despite of the efforts of managers and experts in the field of Geospatial Information System (GIS) and SDI, the results indicating a lack of satisfactory performance. Results of the assessment obtained from BSC model presented an overview of the current situation of the NSDI in Iran. In addition to seeing the results of each criterion, future researches can be conducted to examine more details of each criterion.  To this end, the situation of related sub-criteria can be studied for assessing the strengths and weaknesses of NSDI. Other researchers can add more criteria and sub-criteria. The main reason for the lack of satisfaction in results can be assigned to the restriction in releasing of national geoportal.  It should be noted that the results depend on the definition of sub-criteria and and their weights}, Keywords = {National Spatial Data Infrastructure (NSDI), Spatial Science, Balanced Score Card (BSC), Model Assessment}, volume = {5}, Number = {3}, pages = {151-164}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-357-en.html}, eprint = {http://jgst.issgeac.ir/article-1-357-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {R.A.Bahari, and R.A.Abaspour, and P.Pahlavani,}, title = {Zoning of Particulate Matters (PM) Pollution Using Local Statistical Models in GIS (Case Study: Tehran Metropolisies)}, abstract ={Nowadays, various pollutants have appeared in the air due to the human and biological activities. They threaten citizens’ lives especially in metropolises. These pollutions have direct effect on the health of citizens. Tehran, as the capital city and a metropolis, is engaged constantly with these risks. In recent years, one of the greatest threats for Tehran was suspended particles with a diameter of 2.5 microns, which causes most unhealthy days in recent years. The particles may be of natural origin (e.g., pollen, protozoa, fungi, plant fibers, and dust caused by volcanic activity) or human (e.g., combustion fumes, smoke, metal oxides, salts, oil or tar droplets, silicates, and metal thick smoke). Health studies have shown a significant association between exposure to dust and premature death from heart and lung diseases. In this respect, pollutant concentrations become a major challenge for management in Tehran. The status of the spatial distribution of pollution emissions enables managers to take appropriate actions proportionate to the dangers and risks and reduce risks. In other hand, measuring the concentration of pollutants is costly and performed for points. But, it is necessary that measuring these data where use of them for regional analysis and so then, generalization and distribution of these data to study on city area. Generally both methods are for spatial interpolation and dispersion models to identify and zoning pollutants. In recent years, for development of statistical and geostatistical models, it was available and used multiple spatial interpolation model. Linear interpolation methods use known values around unknown values and estimate these values, but effect of known values on unknown values cause to divide interpolation methods in two broad categories of totally and regionally parts. Frist part, a sheet gives fitness by total dates and in second method it takes place by near points. In Spatial studies, we faced with data that are shifting. They sifted by moving of a region to others. However, in this method of modeling, studied parameters are under independent variables that changed in regions. In these situations if use of statically methods ultimate matrix weight or Final dependence for each independent variable seem values same and, means that in totally method where consider total connect regions equal by each parameter, that in many cases are different with reality and dependency sifting with changing in locations.  In contrast, in regionally method considered a limited area around any sample and estimated weight and relationship between independent and dependent variable(s). In this situation, weights and Dependency ratios are not constant and changed for regionally. So, our observe are very similar and those that are far of each other show Higher spatial dependence. This study used of a Geographically Weighted Regression method for zoning pollutants PM2.5 that is one of local methods. In this method use of land Use, population, elevation, main roads, freeways, temperature, wind and direction speed and Pollutants concentration as input data to model. In the general approach by concentration and know values and a Geographically Weighted Regression estimated weighted matrix and after applied This matrix to the Grid, Evaluated  amounts of concentration. Finally, by Kriging model and concentration, PM2.5 concentrations fitted on Tehran. Finally, this research leads to produce the map of PM2.5 on the city of Tehran, which is useful to identify the risk areas in the city and applying measures to reduce pollution in these areas. Comparing these map produced with the observed data and reviewing statistical parameters such as coefficient of determination (R2=0.75-0.80) and root mean square error (RMSE=7.1-8.5) showed that the proposed model has high ability in estimation the concentration in various areas in the city of Tehran.}, Keywords = {Air Pollution, Mapping, PM2.5, Local Statistical Models, Geographically Weighted Regression, R2}, volume = {5}, Number = {3}, pages = {165-174}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-353-en.html}, eprint = {http://jgst.issgeac.ir/article-1-353-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {V.Sadeghi, and H.Ebadi, and A.Mohammadzadeh, and F.FarnoodAhmadi,}, title = {Change Detection in Multitemporal Remote Sensing Imagery with Thresholding of PSO-Based Fused Change Index}, abstract ={Timely and accurate detection of land cover/use changes is one of the most important issues in land planning and management. Remote sensing (RS) images have become an important data source for change detection (CD) in recent decades. Thresholding of difference image (DI) is a prevalent approach for RS-based CD. It can be shown that the changes in an environment are occurred in such a way that the different spectral changes of phenomenon can be detected in different parts of electromagnetic spectrum. Hence, utilization of several spectral bands can offer a higher accuracy in CD process. However, prevalent thresholding techniques are developed for one-dimensional space and they are not appropriate for CD in multi-dimentional space of RS images. The common approach to overcome this deficiency is to fuse data at feature and/or decision level. Some methods have already been developed for this purpose. Whereas, it is enigmatic to decide which of data fusion technique is the most appropriate one, a common particularity in all these approaches (except: voting and Bayesian) is their supervised nature, as the analyst must determine some parameters which can be the best fit to a certain application and dataset. On the other hand, unsupervised approaches, generally have low accuracy in CD process. In order to develop the thresholding technique to support multi-spectral images, a simple yet effective data fusion approach is proposed in this paper. The developed method is a linear combination of multi-spectral change image based on fusion. Applied weights in linear combination are optimized using Particle Swarm Optimization (PSO) algorithm. The proposed approach consists of the following two major steps. In the first step a multi-spectral change image is generated. Several methods can be used for that purpose. In this research, we chose difference image operation as it is simple to implement and easy to interpret. It includes a simple and straightforward arithmetic difference between the digital values of the two images obtained on different dates.  In the next step, PSO is initialized with arbitrary weights and the weighted image fusion is then carried out as follows: . Where denotes the weight associated to ith band of multi-spectral difference image ( ), such that Afterwards, the OTSU thresholding technique is applied to produce binary change mask (BCM) and evaluate the fitness of the fused change index (FCI). If any of the termination conditions (optimum fitness or maximum number of iteration) is satisfied, the current weights are saved as optimum weights of a weighted linear combination or else they are updated with PSO algorithm to reach the optimum values. The performance of the developed technique is evaluated on a bi-temporal multispectral images acquired by the Landsat-5 Thematic Mapper (TM) sensor in July 2000 and 2009. This data set is characterized by a spatial resolution of 30m×30m and 7 spectral bands ranging from blue light to shortwave infrared (0.45~2.35 µm). It is worth noting that the 6th band of these images (thermal infrared band), is not utilized due to low spatial resolution. The selected area is co-registered subsets of size (470×830 pixels) of two full scenes, including Khodafarin Dam (an earth-fill embankment dam on the Aras River straddling the border between Iran and Azerbaijan). Moreover to visual assessment of CD results, quantitative analysis has been carried out by selecting 2799 samples of changed regions and 5168 samples of unchanged regions, according to field work and image interpretation. The proposed linear combination of multispectral difference images based on fusion which is the development of the thresholding technique to support the multi-spectral images, has better accuracy in CD in comparison with individual spectral bands of DI and the other state-of-the-art image fusion algorithms at feature and/or decision level. Overall accuracy of 90.68% using the proposed method in comparison to an overall accuracy of 79.06% and 70.81% related to the prevalent voting algorithms (data fusion at decision level) and  80.77% related to the Bayesian algorithm (data fusion at feature level), confirms the effectiveness of the proposed method for unsupervised CD in multi-spectral and multi-temporal RS images.}, Keywords = {Change Detection, Remote Sensing Images, Thresholding, Data fusion, Particle Swarm Optimization}, volume = {5}, Number = {3}, pages = {175-192}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-230-en.html}, eprint = {http://jgst.issgeac.ir/article-1-230-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Maboudi, and J.Amini, and M.Saati,}, title = {Analyzing the Effect of Segmentation Method on Road Network Extraction in Urban Areas from HR Satellite Imageries}, abstract ={Road extraction from remotely sensed imageries is a rapid and cost effective method for acquiring transportation information and updating GIS (Geographic Information System) systems. Fast and continuous changes of the urban environment, increase the necessity of regular updating or revising road network layers in GIS systems. The difficulties in the design of an automated road network extraction system using remotely-sensed imagery lie in the fact that the image characteristics of road feature vary according to sensor type, spectral and spatial resolution, ground characteristics, etc. Even for an image taken over a particular urban area, different parts of the road network reveal different characteristics. In the real world, a road network is too complex to be modeled using a mathematical formulation or an abstract structural model. The existence of other objects (e.g., buildings and trees) casts shadows to occlude road features, thus complicates the extraction process. In this research, a general object –based framework for road extraction is implemented, moreover the effect of selection of segmentation method on road extraction is analyzed. Image segmentation is considered as the first and crucial step of objects based image analysis, which aims to obtain the so-called homogeneous segments for succeeding feature extraction, classification, and higher level image analysis. Extensive research has been conducted in the area of image segmentation. Major categories of current state-of-the-art RS image segmentation methods can be classified as follows: 1) point/pixel based; 2) feature based; 3)edge based; 4) region based; 5) texture based; 5) hybrid and so on. Preprocessing as the first step in the proposed method is designed to improve the quality of the image and identify relevant image pixels for further processing. Then, object-based segmentation method is used to extract the initial road segments. Segmented objects are classified into a binary image which represents road and non-road classes. In the next step, skeleton of road objects are extracted. After Skeletonization, a compact approximation of line segments and curves in a vector format are implemented in vectorization step. Small branches in road network, which are produced hitherto and are not known as road, are removed in pruning step; and finally the proposed method is evaluated by comparing with reference road network (as ground truth), which are generated from the road vector data from the GIS or manually extracted road network. For evaluation of the proposed method, real data of Worldview2 sensor in Shushtar area in Khuzestan province-Iran is utilized. Three different segmentation method implemented in eCognition software are tested. In this research, 2 popular quality metrics defined in literatures will be adopted. These metrics include completeness and correctness. Better quality using multi-resolution segmentation method is achieved. Pruning extracted road network leads in above 20% improvement in results. Final results - after multi-resolution segmentation and pruning- show 88% correctness and 85% completeness as evaluation criteria. In addition, selection of multi-resolution segmentation parameters is appraised and the effects of these parameters are assessed. This paper generally emphasizes on the role of image segmentation quality on further processing effectiveness and future works could compare the other state-of-the-art segmentation algorithms with results of multi-resolution algorithm.}, Keywords = {Satellite Imageries, Object Based Segmentation, Road Extraction, Rule Based Classification, Vectorization, Road Properties}, volume = {5}, Number = {3}, pages = {193-202}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-211-en.html}, eprint = {http://jgst.issgeac.ir/article-1-211-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {A.Masjedi, and S.Khazaei,}, title = {Improvement in Detection Performance of Subpixel Targets on Hyperspectral Images Based on Selecting Appropriate Features}, abstract ={A hyperspectral image contains hundreds of narrow and contiguous spectral bands. Because of this high spectral resolution, hyperspectral images provide valuable information from the earth surface materials and objects. Therefore, target detection (TD) is a key issue in processing such data. In fact, the aim of TD algorithms is to find specific targets with known spectral signatures. In the other point of view, the enormous amount of information provided by hyperspectral images increases the computational burden as well as the correlation among spectral bands. Besides, even the best TD algorithms exhibit a large number of false alarms due to spectral similarity between the target and background especially at subpixel level in which the size of target of interest is smaller than the ground pixel size of the image. Thus, dimensionality reduction is often conducted as one of the most important steps before target detection to both maximize the detection performance and minimize the computational burden. This paper presents a method to improve the efficiency of subpixel TD based on selection of appropriate bands using genetic algorithm (GA). To use GA for band selection, two similar fitness function are proposed in this study. The first fitness function is introduced for cases in which the position of target is known. Regarding this, maximizing the output values of TD algorithm on the target pixels is used as the evaluation function. This maximization is roughly equivalent to minimizing the false alarm rate. The main problem in the use of the first fitness function is its need to know the correct position of target pixels in the image. Hence, the second function is proposed to solve this problem. In this function, the output value of TD algorithm is maximized on the simulated targets. In this study, the adaptive coherence estimator (ACE) as the well-known subpixel TD algorithm is used in its local form for the evaluations. Moreover, the target detection blind test data set is employed for the experiments. The data sets includes HyMap reflectance image of Cook City in Montana, USA. The ground resolution of imagery data is approximately 3 m. In the HyMap image, 12 targets, at full and subpixel sizes, were located in an open grass region, which included six fabric panels for the self-test and six for the blind-test. In this study, the local ACE algorithm is implemented using inner and outer detection windows with sizes of 3×3 and 5×5 pixels, respectively. Also, GA is performed with the population number of 100, the probability of mutation of 0.2, the probability of crossover of 0.8, and the maximum generation number of 100. Experimental results obtained for detecting the 10 subpixel targets considered show that, the number of false alarms produced when using dimension reduction by GA is completely low in comparison to that of obtained using all bands. Based on the results, the use of GA with first and second fitness functions reduce the false alarm rate by 95% and 75%, respectively, in comparison to using all bands. For fair comparison with the proposed method, the GA-contrast method is also performed on the same data set. The results show that, compared to the GA-contrast method, GA with the first and second fitness functions reduce the false alarm rate by 94% and 70%, respectively.}, Keywords = {Subpixel Target Detection, Band Selection, Genetic Algorithm, Hyperspectral Images}, volume = {5}, Number = {3}, pages = {203-216}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-336-en.html}, eprint = {http://jgst.issgeac.ir/article-1-336-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {O.Kazemizadeh, and R.A.Abbaspour,}, title = {Extraction of Topological Relations between Regions with Holes in Wireless Geosensor Networks}, abstract ={Geosensor networks are constituted from a large number of nodes that each of these nodes are same sensor-enabled computers. Geosensor network can be imagined as microscopy environmental that gives collection and process of environmental information with specified spatial temporal resolution and high detailed in real time. One of the important applications of these networks is the extraction of topological relation between regions in some phenomena, such as discovery of the causes of forest fires creation, when the topological relation between very hot air, flammable materials, and forest region is converted from “disjoint” to “overlap” and “inside”. Due to existence of cavities in environmental phenomena, marshes or mountains in some regions, these regions must be modeled as regions with holes in geosensor networks. In this research, the regions with holes are monitored by geosensor network and the topological relation between them is extracted. In order to extract topological relation between regions with holes in geosensor networks, an algorithm was designed. Theoretical models, for example 4-intersection, 9-intersection, and RCC are used only for extraction of topological relation between regions that have not any holes, and these models cannot distinguish different topological relations between regions with holes; in designed algorithm, the modified 9-intersection model is used to derive topological relation between a region and another region with a hole. To calculate the this 9-intersection model and extracting relations between these two regions, only it is required that topological relation between the region without hole with each of hole and general region elements of region with holes is determined. In this research, in the first step, 4-intersection model is used and then topological relations between two regions are determined by calculating the modified 9-intersection model. Due to the environment conditions of network, it might not possible to carry out positioning the nodes by GPS; hence, the algorithm will act in such a method that nodes without position obtain topological relation between two regions only based on one-hop neighborhood information. In designed algorithm, decentralized computing system is used and its implementation is evaluated in a simulation. For implementation of designed algorithm, it is required that regions, geosensor network, and communication between nodes are modeled in the simulation program. After modeling of the regions, distributing of nodes is modeled randomly on these regions. It is required that communication between nodes to be possible through neighboring structures. The most basic network neighboring structure is unit disk graph which was used as the default structure in this research. Moreover, it is required to have a coverage structure for merging and integrating of information in the network, that a rooted tree structure is used in this research. Based on the rooted tree structure, one of the network nodes is selected as the root node and other nodes send own local information to the root node in path of tree branches. The local processing is done in each of network nodes and topological relation is calculated locally. Then, the local information was integrated with each other across network nodes and sent to root node. Finally, topological relation between two regions is determined in the root node based on the developed 9-intersection model.}, Keywords = {Geosensor Network, Decentralized Computing System, Topology Relation, Regions with Holes}, volume = {5}, Number = {3}, pages = {217-232}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-379-en.html}, eprint = {http://jgst.issgeac.ir/article-1-379-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Aslani, and M.S.Mesgari, and H.Motieyan,}, title = {An Actor-Critic Reinforcement Learning Approach in Multi-Agent Systems for Urban Traffic Control}, abstract ={Nowadays, most urban societies have experienced a new phenomenon so-called urban traffic congestion, which is caused by crossing too many vehicles from the same transportation infrastructure at the same time. Traffic congestion has different consequences such as air pollution, decrease in speed, increase in travel time, fuel consumption and even incidents. One of the feasible solutions for bringing off the increase in transportation demand is to improve the existing infrastructure by means of intelligent traffic control systems. From a traffic engineering point of view, a traffic control system consists of physical network, control devices (traffic signals, variable message signs, so forth), the model of transportation demand and control strategy. The focus of this paper is on the latter especially traffic signal control. Traffic signal control can be modeled by multi-agent systems perfectly because of its distributed and autonomous nature. In this context, drivers and traffic signals are considered distributed, autonomous and intelligent agents. Besides, due to high complexity arising in urban traffic patterns and nonstationarity of traffic environment, developing an optimized multi-agent system by preprogrammed agent’s behavior is most impractical. Therefore, the agents must, instead, discover their knowledge through a learning mechanism by interacting with the environment. Reinforcement Learning (RL) is a promising approach for training the agent in which optimizes its behavior by interacting with the environment. Each time the agent receives information on the current state of the environment, performs an action in its environment, which may changes the state of the environment, and receives a scalar reward that reflects how appropriate the agent’s behavior has been in the past. The function that indicates the action to take in a certain state is called the policy. The goal of RL is to find a policy that maximizes the long-term reward. Several types of RL algorithms have been introduced and they can be divided into three groups: Actor-Only, Critic-Only and Actor-Critic methods. Actor-Only methods typically work with a parameterized family of policies over which optimization procedures can be used directly. Often the gradient of the value of a policy with respect to the policy parameters is estimated and then used to improve the policy. The drawback of Actor-Only methods is that the increase of performance is harder to estimate when no value function is learned. Critic-Only methods are based on the idea to first find the optimal value function and then to derive an optimal policy from this value function. This approach undermines the ability of using continuous actions and thus of finding the true optimum. In this research, Actor-Critic reinforcement learning is applied as a learning method for true adaptive traffic signal control. Actor-Critic method is a temporal difference method that has a separate memory structure to explicitly represent the policy independent of the value function. The policy structure is known as the actor, because it is used to select actions and the critic is a state-value function. In this paper, AIMSUN, which is a microscopic traffic simulator, is used to model traffic environment. AIMSUN models stochastic vehicle flow by employing car-following, Lane Changing and gap acceptance. AIMSUN API was used to construct the state, execute the action, and calculate the signal reward in each traffic light. The state of the each agent is represented by a vector of 1 + P components, where the first component is the phase number and P is the number of entrance streets which goes to intersection. Also, the action of the agent is the duration of the current phase. The immediate reward is defined as the reduction in the total number of cars waiting in all entrance streets. In fact, difference between the total numbers of cars in two successive decision points is used as a signal reward. The reinforcement learning controller is benchmarked against optimized pretimed control. The results indicate that the Actor-Critic controller decreases Queue length, travel time, fuel consumption and air pollution when compared to optimized pretimed controller.}, Keywords = {Actor-Critic, Adaptive Traffic Signal Control, Multi-Agent Systems, Reinforcement Learning}, volume = {5}, Number = {3}, pages = {233-246}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-344-en.html}, eprint = {http://jgst.issgeac.ir/article-1-344-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Y.Delaviz, and J.Karami, and M.Shaygan,}, title = {Using NSGA-II for Multi-Objective Optimization Allocation of Urban Land Use in Order to Reduce Earthquake Vulnerability}, abstract ={The occurrence of earthquake has made human being consider fundamental plans to reduce the consequent danger and destruction. The only means to reduce the vulnerability is to set specific management of urban crisis in construction; moreover, this aim cannot be achieved unless the city immunity, in confrontation of the earthquake, is considered as a major purpose in all stages of urban planning. Proper allocation of various urban land uses helps hugely the process of management of urban crisis related to the earthquake; accordingly, recognizing the different effective variables of vulnerability of urban areas from the aspects of urban land use, definition and declaring their relations with vulnerability, their analysis and finally preparing the land use optimizing maps with less percentage of vulnerability, is the principle target of this paper.  In this paper, for optimizing urban land use allocation, with the approach of reducing the vulnerability caused by the earthquake based on the physical factors, the multi-objective optimization algorithm NSGA-II was used for modeling. The 12th district of Tehran was taken as the subject of study. In this algorithm the main objectives include: maximizing compatibility of adjacent land uses, accessibility of land uses, availability of sanitary-medical and residential land uses to the Road network and minimizing susceptibility in earthquake;#39s time and Minimizing land uses change. Considering the fact that the NSGA-II algorithm is multi-objective, the decision maker encounters different solutions in the Pareto-optimal front, which makes the process more complicated. Accordingly, to aid the decision making process and presenting the correspondent scenarios with the decision makers;#39 priority, the clustering analysis was used with K-means approach. To study the changes of the results of different implementations of algorithm and stability of optimization algorithm, convergence trend and repeatability test carried out. In the resulted optimized land use arrangements, the levels of objective functions are much better than the previous arrangement. Moreover, accessibility objective function has been improved mostly under the effect of optimization (27 %). The average percentage of the improvement of the objective functions in the algorithm was 19 %. In the repeatability test, the average percentage of the overlay of the algorithm;#39s solutions in different runs was recorded as 76 %, which can be recognized as a proper value, and represents the suitable repeatability of the algorithm. The results were found acceptable based on the convergence trend, by having the stable value of the objective functions after specific times of iteration. Several factors represent the efficiency of the model which can be named as; the proper method of optimization that was compatible with the problem, defining the objective functions based on the reality and including the main aspects of the problem  of the earthquake vulnerability  in the presented model, concerning the opinion of the decision makers in the process of the research and the final stage for selecting the optimum arrangement with the analysis of the results of the scenarios and the scenarios;#39 clustering. The results of this research can be an aid as a means to support deciding for the planner and urban management policy makers encountering earthquake, in planning appropriately for the urban spaces.}, Keywords = {Multi-Objective Optimization Algorithm, Land Use Allocation, Non-dominated Sorting Genetic Algorithm-II, Geospatial Information System, Clustering, Earthquake Vulnerability, Tehran}, volume = {5}, Number = {3}, pages = {247-264}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-387-en.html}, eprint = {http://jgst.issgeac.ir/article-1-387-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Izadi, and A.Mohamadzadeh, and A.Haghighattalab,}, title = {Design and Implementation of a System for Roads Occlusion Detection Using Fuzzy Logic and Support Vector Machine (Case Study: Bam Earthquake)}, abstract ={Damaged and devastated roads recognition and determination of their damage degree seem to be vital when they are affected by a natural disaster like an earthquake. This damage and obstacle is as a consequence of debris caused by collapsed building adjacent to the roads. Moreover, it is essential due to the emergency nature of facing such phenomenon. This study makes use of a new approach for the semi-automatic detection and assessment of marred roads in an affected urban area which is utilizing pre event roads vector map and both pre and post disaster Quick Bird satellite images. In this research, we need to explain a definition for the Damage. As a matter of fact, this damage or obstacle can cause any sort of disturbance on the functionality of the roads network such as conducting rescuers, retrieving survivors, and reconstruction operations. Indeed, in most of urban areas, the width of roads is not that wide, particularly in the third world countries and undeveloped areas. Thus, any trivial obstacle or extra object can cause a noticeable disturbance for transportation. Therefore, this damage is defined as both Debris engendered by collapsed buildings or any other urban structures, and the observation of parked cars on the surface of the roads in devastated areas. To illustrate, this method consists of two main steps; damage detection (by classification), and damage assessment. In this case, many different features are considered for classification of roads surface. These features are such as shadow index, NDVI, and GLCM based features. Furthermore, an appropriate Genetic Algorithm (GA) is designed and used to analyze and find the best set of optimal features. Given that there would be a potential defected band or any correlation among the features, this issue gives useless and unessential information to the classifier and increases the computations time and decrease the accuracy. Afterwards, with making use of these optimal features set and after trial and error between two well-known and prevalent classifiers (SVM and MLL), the supervised Support Vector Machine classifier was selected. It is because of gaining higher overall accuracy and enhancing the damage detection consequently. Thus, SVM is applied to the optimal features to detect damage (damage detection step). Since, a road is a slim object and to analyze the obstacle of this slim object more meticulously, it is needed to divide it into smaller parts. After dividing each individual road to several and equal partitions, a designed ‘Mamdani’ fuzzy inference system (FIS) is represented for the road damage assessment step. These three damage levels are including Low, Medium, and High damage levels. It is based on each small partition. That says, each partition goes into the Fuzzy inference system as a point and the output is the partition damage level index. Afterwards, some statistic criteria are considered on the number of different damaged partition and the damage level is generalized on each individual road. Therefore, each single road gets a damage label and lead to a roads damage level map. The proposed method was tested on QuickBird pan sharpened image from the Bam earthquake and the results indicate that an overall accuracy of 92% and a kappa coefficient of 0.9 were achieved for the damage detection step, and 82% of the roads were labeled correctly in the road damage assessment step. The obtained results show the efficiency and accuracy of the current algorithm.}, Keywords = {Genetic Algorithm, Fuzzy Inference Systems (FIS), Road Damage Assessment, Support Vector Machine, Quick Bird Image}, volume = {5}, Number = {3}, pages = {265-278}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-202-en.html}, eprint = {http://jgst.issgeac.ir/article-1-202-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {A.Rajabi, and M.Momeni,}, title = {Urban Buildings Changes Detection in 1:2000 Map Using GeoEye1 Satellite Stereo Images}, abstract ={Nowadays satellite imagery uses for producing and updating the maps because of their capabilities. In recent years, IRS-P5 images was used for updating the maps with 1:25000 scale. Also VHR images like IKONOS2 and QuickBird2 can be used for updating cadaster maps based on manual transformation. At present With easier access to these images and also appearance of VHR imagery like GeoEye1 and WorrldView2 and extension of advanced algorithms create a good opportunity for making large scale maps and speed the updates. It can be said that with using VHR images, updating maps is done better than making maps, so it’s in priority. But using satellite images and processing algorithms for making and updating large scale maps have some difficulties in preparing required layers in these kind of maps. Even GeoEye1 images that have 50cm spatial resolution, can’t prepare all of required layers. The main purpose in this theses is updating 1:2000 scale maps using GeoEye1 stereo image. Indeed we want to study the performance of these data for updating the maps with creating feature vector for image pixels instead of gray values and also using GeoEye1 stereo image instead of single vertical image. Our first assumption is that if we use GeoEye1 stereo image for new image instead of single vertical image, not only we can get higher precision for updating large scale maps, but also we can manage different height error and making shadows. For this purpose we used GeoEye1 stereo image. Our second assumption is that in updating large scale maps, GD-making of gray scales is no longer effective because our subject is referred to geometry of phenomenon. For this purpose, first all of features are extracted from image, then participate in GD-making and finally the most effective features in 3 groups are chosen and arranged with try and error that make a feature vector with independent members. In the beginning of work, first horizontal and vertical accuracy that required for large scale maps are reviewed, then the largest scale map that can be prepared with satellite images are selected (in this case is 1:2000) and finally the performance of GeoEye1 stereo images between 2006 to 2010 that used for building change detection and update 1:2000 scale maps are reviewed. Updating strategy for 1:2000 scale map that used in this theses has 5 stages: choosing data and pre-processing them, change detection, post-process the change detected results, assessment the change detected results and finally applying the results in maps. For these 3 stages; change detection, post-process the change detected results and assessment the change detected results; we written an algorithm based on differentiation of image pixels feature vector that detected building changes in 3 study regions, Additional pixels are eliminated and these changes detected by algorithm are compared with actual changes using confusion matrix and the results are showed In the form of Overall accuracy, producer accuracy and user accuracy. Accuracy Values obtained for change class in best condition for second region that was an area with low building density is 3.11%, 68.60% and 64.29%. But in third region that was an area with high building density, the acquired accuracies for changes class are 95.07%, 4.81% and 5.22%. Based on these results for change detection using GeoEye2 stereo images, suggested algorithm has necessary performance in the areas with low building densities. Also it proofs that gray scale deferential or any other image feature alone doesn’t perform well in change detection using VHR images. But using feature vector in GD-making is quite effective. And also we have been able to manage the error due to the height difference and shadows and introduce these parts to operator using stereo images.}, Keywords = {GeoEye1 Satellite Stereo Images, Updating, Changes Detection, Urban Large Scale Maps, Confusion Matrix}, volume = {5}, Number = {3}, pages = {279-292}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-273-en.html}, eprint = {http://jgst.issgeac.ir/article-1-273-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {M.Akbari, and F.Samadzadegan,}, title = {Urban Air Pollution Pattern Mining Using an Extended Spatial Co-location Data Mining Method}, abstract ={Air pollution in cities is one of the most problems that effects on human health, environment, economy, urban management and etc. urban managers to overcome to this problem, should determine affecting parameters and also the way that they impact on air pollution to arrange necessary plans for it. Different researches have been assessed parameters such as meteorology, traffic and topography impacts on the air pollution. Identification of affecting parameters on air pollution in urban regions using co-location pattern mining can help to solve this problem. Co-location pattern represents a subset of spatial objects that their instances usually are in close proximity. Existing methods with shorthcomings such as applying only one feature-type, considering spatial relationships explicitly as input data and extracting patterns without any emphasis on a specific object aren’t appropriate to applications such as air pollution. Then, in the present research developed a new co-location pattern mining model so that it can handel mentioned shortcomings. In this research tried to consider affects of all three before mentioned parameters simultaneously on air pollution by extracting prevalent patterns. In this literature to develop the mentioned model, we defined a framework for a data mining problem. As there was a gap in existing literature for considering different feature types, new metrics have been defined to compute the participation ratio for all point, line and polygon data. Actually, the applied metric for point data is the available one but the other ones for line and polygon data have some extensions based on neighborhood to compute these metrics. As the air pollution is a serious problem for Tehran, the developed model implemented and tested on part of Tehran’s data. To apply the proposed method, we classified each of the studied parameters to three different classes (low, normal, high) based on their physical characteristics. The data of 4 days in Farvardin, Tir, Mehr and Dey months selected and used to first, check repeatability of results and second, based on changes in seasons, control the validity of the proposed model. The input value for neighborhood radius is 1500 meter and for prevalence threshold is 0.5. The neighborhood radius is selected based on the average distances between air pollution stations and meaningfully of parameters changes. Also, the prevalence threshold was selected to find patterns which at least half of its instances participate in the pattern. The assessed results of extracted patterns first show the ability and correctness of our proposed model and second represent that medium and high air pollutions produce meaningful patterns with low traffic volume, low wind speed and also low topography. Also, their attitude is towards central regions of our case, region 6 of Tehran. Finally, it is necessary to mention that the air pollution is a spatio-temporal problem and in addition to spatial dimension, we should have an attention to temporal aspect. But in this research, the emphasis is based on spatial extension of model to apply for all feature types. Extending the proposed model to mine spatial and temporal patterns simultaneously is the goal of researchers.}, Keywords = {Spatial Data Mining, Co-location Pattern, Air Pollution, Tehran}, volume = {5}, Number = {3}, pages = {293-307}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-59-en.html}, eprint = {http://jgst.issgeac.ir/article-1-59-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {AzmoudehArdalan, A. and Karimy, R. and Mehrnegar, N.}, title = {Improving Long-Wavelength Gravitational Field via Satellite Altimetry}, abstract ={Frequency decomposition of the Earth gravitational potential in terms of spherical/ellipsoidal harmonics has been of the significant matter for a wide range of applications such as the geodetic, oceanographic, and geophysical purposes. These days, thanks to the notable advancement in the field of satellite altimetry, monitoring the sea level on the global scale has been realized in practice. Meanwhile, the gravity information may be derived from these valuable data if the accurate mean dynamic topography has been obtainable. In this respect, the mean dynamic topography can be determined via the oceanic or geodetic approaches. In the oceanic manners, the mean dynamic topography is derived using the oceanic information such as salinity, temperature, and surficial currents; while according to the geodetic methods one can obtain the mean dynamic topography by integration of satellite altimetry measurements and global geopotential models. In this contribution, we aim at assessing the feasibility of improving the pre-existing geopotential models by means of satellite altimetry observations. To this end, the sea surface topography is estimated using the geopotential and mean sea level models in a constrained least squares sense. As such, we can arrive at the Gauss-Listing geoid over the sea areas derived from the computed sea surface topography and the known mean sea level values. The Bruns formula is then implemented to reduce the resultant geoid into the gravitational potential values over the oceans on the surface of the reference ellipsoid. On the other hand, the gravitational potential values over the continental regions are obtained on the surface of the reference ellipsoid via the geopotential model of interest, once the topographic bias corrections have been considered. Lastly, a new point-wise geopotential model in terms of spherical harmonics is developed through application of the spherical harmonic analysis to the worldwide gravitational potential. As the case study, the presented methodology has been evaluated so as to improve the two global geopotential models, namely EGM2008 and go_cons_gcf_2_dir, up to the degree and order 90. Accordingly, the DTU10 mean sea level model, which has been derived from information of Topex/Poseidon, ERS1, ERS2, ENVISAT, Geosat, GFO, and Jason satellites, has been applied to the EGM2008 and go_cons_gcf_2_dir models in order to estimate the sea surface topography based on the proposed optimization solution. Consequently, the improved versions of the global geopotential models have been developed thanks to the application of the harmonic analysis to the gravitational potential values that have been attained over the sea and land areas on the surface of the reference ellipsoid. Based upon the numerical results of the assessment of the developed models at the first-order GPS/leveling points within the test areas in Iran and Finland, the capabilities of the proposed method in deriving enhanced geopotential models have been asserted. Moreover, the comparison of the consequential enhanced models with respect to the BGI gravity points have demonstrated the efficiently of the method throughout the world. As a whole, we have deduced that the presented method can be applicable to significantly improve an extensive range of the global geopotential models.}, Keywords = {Geopotential Models, Satellite Altimetry, Mean Dynamic Topography, Geoid Height, Fourier Analysis}, volume = {5}, Number = {4}, pages = {1-10}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-93-en.html}, eprint = {http://jgst.issgeac.ir/article-1-93-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Tatar, N. and Saadatseresht, M. and Arefi, H. and Hadavand, A.}, title = {Developing New Index to Object-Based Shadow Detection from High Resolution Satellite Images over Urban Area}, abstract ={Height variation in different urban objects e.g. buildings and trees coincide to occurrence of shadows in aerial and satellite images. Areas casted by shadow, appear darker than neighbouring areas in the image and it makes an unwanted contrast to the other brighter areas. This phenomenon attenuate different expectations from remote sensing data. In particular shadow areas ruins the result of automatic image matching algorithm and in land cover classification cause the misclassified pixels. Detection of overshadow areas is the primary step to deal with this problem. Different strategies have been used to detect shadow in remote sensing images. To name a few we can consider classification based methods, region-growing methods and different spectral indices. In classification based method, some ground truth from shadow areas are collected and supervised machine learning algorithms are used to classify shadow and non-shadow pixels. Region-growing algorithms use the high contrast between shadow and bright areas. Spectral indices are made by simple arithmetic equations between spectral bands. There is some deficiencies in the result of previous methods and strategies. In machine learning methods, existence of ground truth information is essential and somehow affect the results. Using region growing and spectral indices usually leads to addition of roads and vegetation to shadow areas. The result of all this methods are presented in pixel level, labelled shadow pixels. Wrongly detected shadow pixels appear as noises in classification map. In high resolution aerial and satellite imagery single pixels are not meaningful independently. This is the outcome of decreasing the ground sampling size of sensors versus natural objects on the earth. The solution to deal with this problem is to integrate similar neighbouring pixels which belong to the same ground object. Object-based image analysis (OBIA) is developed based on this idea, considers image object, created by segmenting the image, as processing unit. The power and possibilities of image objects are less discussed and considered in detecting shadow areas. In this paper we propose a new object-based framework for shadow detection which simultaneously benefits from OBIA, machine learning and spectral indices. Our proposed framework consists of four main steps. First step is the pre-processing of data. In this step spectral bands are pan-sharpened to enhance the spatial accuracy and the panchromatic band is segmented by eCognition Software. In the second step new spectral indices are proposed to overcome the weakness of existing indices in mixing roads and vegetation to shadow areas. To automate the process of detecting shadows from index values the Otsu thresholding algorithm is employed. Third step is object-based shadow detection. To detect shadow areas in object level, majority analysis of shadow pixels in each image object is considered. To solve the ambiguity between vegetated and shadow objects an extra condition is checked to confirm that an object belongs to shadow class. This condition uses the mean NDVI value of pixels in each image object. Finally in the fourth step evaluation of produced map is obtained using completeness, correctness and F-measure. In this step the result of shadow detection using spectral indices, proposed index, machine learning and proposed method are compared and analysed. GeoEye-1 satellite data comprised 4 spectral bands over Qom city in Iran is used in the experiment. 800 shadow objects are selected manually to evaluate the result. Correctness, completeness and F-measure obtained from confusion matrix of shadow map are calculated to compare the results. The result of shadow detection by spectral indices and SVM and random forest classifiers have been compared to the result of proposed method. Result of our experiments demonstrates the superiority of proposed object-based over the pixel-based method respect to correctness and F-measure for different classifiers. The proposed method succeed to detect shadow area with 93% correctness and 92% Completeness It is also evident that object-based method have well behaviour on the edge of shadow areas and perfectly detect shadows.}, Keywords = {Shadow, Shadow Detection, FNEA Segmentation, Object-Based Classification, High Resolution Satellite Imagery}, volume = {5}, Number = {4}, pages = {11-21}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-337-en.html}, eprint = {http://jgst.issgeac.ir/article-1-337-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Jahandideh, S. and SaadatSeresht, M.}, title = {Single Kinect Data Fusion for 3-D Modeling of Human Body}, abstract ={Three-dimensional modeling of the human body has become one of the important research topics in computer graphics. This is due to the importance of virtual representation of the human body in applications such as animation, computer games, virtual fitting room and cases etc. This has been obtained in the context of software and hardware developments in computer graphics. In this regard, three-dimensional modeling of the human body with low cost, high quality and accessible to everyone without the complexity and the need for specific expertise for processing is of great importance. The aim of this paper is proposing a method for solving 3D human body modeling. Since the introduction of Kinect by Microsoft with features including low cost, no complexity, depth and color images production with a high frame rate and possibility of using in different lighting conditions, it could be a useful tool for our this purpose. But using the Kinect sensor for human body modeling confronts challenges such as raw data with low resolution and high noise, users movement during the scan, hidden areas and also a lack of accurate connection between depth and color data. In this regard, the idea of using the Kinect rotation motor in vertical angles in order to reduce the distance from the user to increase the quality of primary data was presented. The proposed non-rigid registration method was utilized for solving the problem of user instability during the scan. Also the sensor geometry calibration for accurate alignment of color and depth data was used. In this paper, the procedure for 3D human body reconstruction is as follow: at the first, person is stayed on a specified distance from the Kinect and is scanned in the eight stations at three vertical angles. Then, colored point cloud are achieved by aligning color and depth images and extracting user data from background. Then rigid registration between sequential data stations is performed automatically. In order to solve the problem of instability during the scans, non-rigid registration is done between data station pairs. Finally, a general mesh was generated from the final point cloud and texture mapping is done to produce a realistic 3D body model. The experimental results show that our rapid 3D human body modeling system has a high capability comparing to other similar systems. This system has a lower cost (less than 150 dollars), capacity of scanning in near distance without additional equipment such as rotation tables, and a higher quality of the final 3D model so that the details such as wrinkles and hair style is recognizable. The final 3D model generated from point cloud with about 4 mm density and 4mm noise thickness. Also the problem of low-quality in modeling of legs and shoes caused by a high movement during the scan, have been largely resolved. Therefore, we can generally say that the proposed method resolves the similar system’s weaknesses in data collection and processing steps. This makes our system proper for diverse applications and different environment.}, Keywords = {3D Human Body Modelling, Kinect Sensor, Non-Rigid Registration, 3D Scanner}, volume = {5}, Number = {4}, pages = {23-35}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-338-en.html}, eprint = {http://jgst.issgeac.ir/article-1-338-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Abdi, N. and AzmoodehArdalan, A. R. and Karimi, R.}, title = {Evaluation of Iran Ionosphere Model Based on GPS Data Processing}, abstract ={The ionosphere is the ionized region of the atmosphere which is situated between 80 and 1200 km. Ionospheric delay is the major resource of error in GNSS positioning, Therefore knowledge of the ionospheric behavior is an important factor in this field. Total Electron Content (TEC) values may be considered as a key parameter to monitor the behavior of the ionospheric medium. Nowadays, continuous GNSS observations can provide an efficient tool to monitor timely ionospheric irregularities. Many scientists have investigated global ionospheric models on the basis of different observations data. For example, IGS Ionosphere Working Group produced daily TEC maps for user services from GNSS data. In this paper, we intend to utilize dual frequency GPS observations provided by Iranian Permanent GNSS Network (IPGN) to calculate TEC maps in Iran. For this purpose, data of 43 IPGN stations and about 180 IGS stations were processed with Bernese GPS software. This process was based on the use of spherical harmonics expansion up to degree and order 15 like the global one, to provide a model of TEC. In the meantime of using GPS data to calculate TEC maps, other resource of errors in GPS positioning such as satellite and receiver clock biases, tropospheric error and multipath error must be either removed, or at least significantly reduced. For this purpose, we used the geometry free linear combinations of pseudo ranges and carrier phases. For reducing the noise level of pseudo range observations we used the carrier phase smoothed pseudo range data as well. The processing method consists of several steps; code smoothing with phase observations, estimation of Differential Code Biases (DCBs), estimation of spherical harmonic coefficients and generation of TEC maps. Before code smoothing, the phase observations were pre- processed to remove the cycle slips. The used model assumes that the whole free electrons are concentrated on a thin spherical layer to an altitude varying between 250 and 450km. We chose the altitude equals to 450km in this paper. The obtained results show that the maximal TEC value measured over Iran is about 22 TECU, this value corresponds to the noon period (midday), where the sun is close to the zenith. The minimal TEC value varied around 5 TECU, it corresponds to the midnight period, and such values were obtained for the day of Jun 22, 2009. Iranian Ionosphere Model (IRIM) was created and compared with the different solutions delivered by the several IGS Ionosphere Associate Analysis Centers (IAACs) which are CODE, ESA, JPL and UPC. Despite different IAACs use various approaches, they provide TEC maps with resolution of 2 hours, 5◦ and 2.5◦ in UT, longitude and latitude respectively. In order to compare our obtained results with different IAACs TEC maps, we chose TEHN station from IPGN stations to generate and display TEC profiles. The differences between the various models are less than 6 TECU. The IRIM results had minimum differences with CODE TEC maps which both use spherical harmonics as their basic functions. The remained differences caused by the fact that when CODE TEC maps are estimated, the data from IPGN stations are not used. Calculated TEC values were thereafter applied to correct and improve the quality of the single frequency solutions in absolute and relative positioning modes. It is noted that ionosphere free (L3) solution results was considered as the reference solution. In absolute mode, we received the considerable improvements in horizontal and vertical components by using the IRIM instead of IGS models. In relative mode the comparison between the corrected L1 and L3 solutions showed that ignoring the ionospheric effects causes network contraction. Furthermore, the corrected L1 solution results using IRIM rather than IGS models were closer to the L3 solution results. Moreover, for baselines up to several hundreds of kilometers, deviations were better than 10cm in horizontal component.}, Keywords = {Positioning, Ionosphere, Spherical Harmonics, GPS, IPGN, TEC ,DCB }, volume = {5}, Number = {4}, pages = {37-47}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-315-en.html}, eprint = {http://jgst.issgeac.ir/article-1-315-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Shamshiri, M. and AkhoondzadehHanzaei, M.}, title = {TEC Anomaly Detection before Strong Earthquake Using Artificial Neural Network}, abstract ={Discussion about earthquake to reduce its casualties and damages is very important, especially in the Seismicity area like Iran that the occurrence of this natural phenomenon is seen annually. Iran has an approximate area of 1648000 square kilometers with geographical coordinates 25 to 40 degrees north latitude and 44 to 64 degrees east longitude that located in the middle of Alpine-Himalayan seismic belt. In this erea there are many active faults that their movement continues and the final balance has not been established. The occurrence of severe earthquakes as Buin Zahra earthquake (1962), Tabas (1978), Rudbar (1990), Bojnoord (1997), Bam (2003) and other numerous earthquakes prove this subject. While most natural disasters are out of human control, but it seems that Success in prediction of temporal and local of them can dramatically control damages and casualties. Earthquake occurrence in addition to changes of geometry and physics of the earth crust has many other effects. Some of its effects is in the ionosphere layer that are indicated as changes in the electrons values, ions density and electromagnetic field. Anomalies detection before earthquake is an important role for earthquake prediction. Each geophysical and geochemical parameter of the lithosphere, atmosphere and ionosphere layers that unusually changes before earthquake are known as earthquake precursor. Ionosphere changes that recognition by remote measurements (such as using Global Positioning System (GPS)) are known as earthquake ionospheric precursor. TEC (Total Electron Content) of the ionosphere can be achieved by GPS data processing. Classic methods such as mean are unable to detect non linear pattern and therefore in complex and nonlinear systems they are not suitable for recognition and prediction of time series. Because of the nonlinear behavior TEC  and land surface changes in order to detect changes, in this paper an attempt is done using an artificial intelligence method including ANN (Artificial Neural Network) and multilayer Perceptron (MLP) for pattern recognition and prediction of TEC variations. Because ionospheric fluctuations usually do not have a normal distribution and do not follow Gaussian curve, in order to detect seismic anomalies, the mean and interquartile range is used to determine the lower and upper bounds. In this study several data sets from the ionospheric  total electron content (TEC) derived from the GPS data processing by Bernese softwares. In this way earthquakes of Ahar located in east Azerbaijan (2012/08/11) and Bushehr (2013/4/9) have been studied and the results were compared with data from global stations. First the stations coordinates were calculated using Bernese software with PPP (Precise Point Positioning) method. Then TEC values were obtained using GIM (Global Ionosphere Model). By analyzing the causes of ionospheric anomalies such as the geomagnetic field and solar activity and remove them from the process, results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms could be useful for the prediction of nonlinear time series and outstanding anomalies ocurr some days before and after earthquake. It can be concluded that ANN algorithm has been able to detect TEC anomalies well. Also TEC values are obtained from ground stations have a high correlation with the results of global standard model.}, Keywords = {Earthquake, Ionosphere, Anomaly, TEC, Artificial Neural Network}, volume = {5}, Number = {4}, pages = {49-58}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-309-en.html}, eprint = {http://jgst.issgeac.ir/article-1-309-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Saadat, S. A. and Safari, A.}, title = {Gravity-Field Recovery of the Earth Based on Sparse Approximation of Spherical Harmonic Coefficients Using Stabilized Orthogonal Matching Pursuit Algorithm}, abstract ={Gravity-field recovery of the Earth using reconstruction of spherical harmonic coefficients up to specified degree and order requires proper data sampling based on Shannon-Nyquist rate. Since, these coefficients are globally significant, the sampling must be done uniformly on the Earth, which it takes much time and expense to collect and process data. Many studies have been done in the field of sampling analysis of spherical harmonics [1,2]. Sneeuw [2] showed a lack of Nyquist sampling rate can cause aliasing of second type in gravity-field modeling. Recently based on Compressive Sensing (CS) theorem the sampling rate can be substantially reduced and a signal can be approximated in sparse sense with fewer sampled data that has main role in reconstruction. In this case, the desired signal can be reconstructed, using only some base functions, which are most strongly correlated with the problem. Therefore, based on this strategy, the base functions posing the best solution to the problem will be selected and the sampling rate for regional gravity field modeling will be decreased significantly. When we say a signal is m-sparse, it means that there are at most  nonzero components in the signal. In this case, only m coefficients of the signal have large magnitude, and others are zero, or have very small values. Here, the desired signal can be reconstructed with its large components without loss of more information. The zero-norm of a vector which is defined as , specifies the sparsity-level of a signal. Sparse approximation has been discussed in many studies [4,5,6,7,8,9]. The basic idea proposed by Mallat and Zhang [4] is called matching pursuit (MP), which is an iterative sparse approximation method to reconstruct a signal under specified conditions by replacing a complex sparse problem with a simple optimized solution. Pati et al. [6] modified this algorithm into orthogonal matching pursuit (OMP), which is used for non-orthogonal dictionaries and converges faster than MP. The regularized orthogonal matching pursuit (ROMP) algorithm popularized by Needell and Vershynin [8] is an iterative sparse approximation method where at each iteration m nonzero components of unknown parameters that most closely resemble the properties of the desired signal are selected. Needell and Tropp [9] refined the ROMP algorithm with compressive sampling matching pursuit (CoSaMP), which identifies locations of the large energy of a signal at each iteration. All these algorithms try to find column vectors in the design matrix that most strongly correlate with the desired signal. It is also assumed that the design matrix is well-posed and prior knowledge of the sparsity-level of the signal is clear. Usually, in practical application an ill-posed problem may be encountered, also the sparsity-level of the signal is not exactly clear, which make it difficult to use conventional iterative methods of CS. In this paper we present a new dynamic algorithm called Stabilized Orthogonal Matching Pursuit (SOMP) for gravity-field recovery of the earth using sparse approximation of geopotential spherical harmonic coefficients, which is compatible with the ill-posed problem and can determine the sparsity-level of the signal, properly. Numerical result of the calculated spherical harmonics coefficients up to degree and order 36 shows that the algorithm is able to reconstruct the Earth's gravity-field with precision in mind the number of samples is 50% lower than the Nyquist rate.}, Keywords = {Gravity-Field Recovery, Spherical Harmonics, Ill-Posed Problem, Compressive Sampling and Sparse Approximation}, volume = {5}, Number = {4}, pages = {59-71}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-360-en.html}, eprint = {http://jgst.issgeac.ir/article-1-360-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {AshrafzadehAfshar, A. and Joodaki, Gh. R. and Sharifi, M. A.}, title = {Evaluation of Groundwater Resources in Iran Using GRACE Gravity Satellite Data}, abstract ={Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided a powerful tool to evaluate groundwater resources. In many cases, groundwater resources are nonrenewable, and monitoring the rates at which they are utilized is important for planning purposes. In this study, we have used GRACE level 2 Release 05 data to evaluate groundwater resources across southern Iran (south of 34o latitude) during August 2002 to December 2010. We estimate monthly changes in total water storage (groundwater plus soil moisture plus surface water and snow) across this region using data of GRACE level 2 Release 05, from the Center for Space Research (CSR) at the University of Texas (data available at http://podaac.jpl.nasa.gov). We replace the GRACE results for the degree-one spherical harmonic coefficient, which correspond to geocentre motion due to the Earth’s mass redistribution, with those computed as described by Swenson et al. [2008]. We also replace it for the lowest-degree zonal harmonic coefficient, C20, which is due to the flattening of the Earth, with those obtained from Satellite Laser Ranging (SLR). The effects of Glacial Isostatic Adjustment (GIA) are small in this region, but are nevertheless corrected by a GIA correction model. Stripping effects in the GRACE data, due to the nature of the measurement technique in GRACE and mission geometry, are smoothed by applying a Gaussian smoothing function with a 350 km radius. The results show a large negative trend in total water storage centered over western and southern Iran. GRACE data have no vertical resolution, in the sense that it is impossible to use the GRACE data alone to determine how much of the mass variability comes from surface water or snow, how much comes from water stored in the soil, and how much comes from water in the subsoil layers (i.e., from groundwater). Because our goal is to isolate the changes in groundwater storage, it is necessary to first remove estimates of the other water storage components. Using output from a land surface model such as a version of Community Land Model (CLM4.5) to remove contributions from soil moisture, snow, canopy storage, and river storage, we conclude that most of the long-term water loss in the southern Iran is due to a decline in groundwater storage. Our estimates show that the groundwater loss during this period is at an average rate of 45 km3/yr. We compare our GRACE estimates over southern Iran with Iranian groundwater estimates obtained from 330 active observation wells, used to monitor the level and quality of groundwater across this region. The results show that the conclusion of significant Iranian groundwater loss is further supported by the in situ well data. These estimates represent the combined effects of natural climate variability (e.g., drought) and human activities. Because CLM4.5 also includes unconfined aquifer storage, we can estimate anthropogenic groundwater trends by subtracting the CLM4.5 predictions of naturally occurring groundwater change from our total groundwater change estimates. These results indicate that 2.99 ± 1 km3/yr of the groundwater loss in southern Iran may be attributed to human withdrawals.}, Keywords = {GRACE Data, Groundwater, Well Data, GLDAS Model, CLM4 Model}, volume = {5}, Number = {4}, pages = {73-84}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-381-en.html}, eprint = {http://jgst.issgeac.ir/article-1-381-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {JavadiAzar, R. and Voosoghi, B. and GhaffariRazin, M. R.}, title = {Earth Surface Deformation Analysis in Iran using Novozhilov’s Mean Rotation Measure with Finite Element Approach}, abstract ={One of the most fundamental and important a new area of research in geodesy is earth surface deformation modeling at local and global scales. Also, check out the effective factors in deformation, and offers various computation methods in order to determine the movement of the Earth's crust are considered as a recent development in geodesy. In recent years, space geodetic techniques with high precision and reliability have provided new sources of information to determine the geodetic positions. This information used for the detection and quantification of surface deformations. Iran is located in a very active seismic region. Cataloge of historical earthquakes in this region shows that Iranian plateau has potential for great earthquakes in the future. Due to this high risk of natural hazard, many researchers have focused to study about geodynamic of Iran. For this purpose, in this paper a new numerical measure called Novozhilov measure of mean rotation is introduced. In the fourth decade of the 20th century Novozhilov obtained a measure of the mean rotation by modifying a previous definition produced by Cauchy. The measure introduced by Novozhilov for the mean rotation indicates the importance of the infinitesimal rotation tensors. To achieve this goal, first linear strain and rotation tensors on earth surface based on shell theory in continuum mechanics using finite element approach will be calculated and then the mean rotation measure using linear strain and rotation tensor components is determined. In this paper the results of Novozhilov’s mean rotation measure were compared with GPS block rotation rates in deg/Myr measured in the center of each block (from block model with locked faults). GPS network that is used in this paper includes 37 stations all over Iran. The computed linear strain and rotation tensors based on geodetic observations (GPS) of national permanent geodynamic network in 2008 are in a good agreement with the numerical results of previous works. The pattern of Novozhilov’s mean rotation measure over Iran shows that the highest right turn rotation is related to the region in the south of Iran including JASC, BABS, GLMT (3.113deg/Myr) stations. Also the highest left turn rotation can be seen in the north of Iran including MAVT, BIAJ, GRGN (-2.509deg/Myr) stations. The importance of Novozhilov’s mean rotation analysis on earth surface in comparison to the analysis of this measure in Cartesian system is shown by this fact that the computed measure on the earth surface is in a good agreement with the results of previous studies on blocks rotation in different areas of Iran.}, Keywords = {Earth Surface Deformation Analysis, Strain Tensor, Rotation Tensor, Mean Rotation (Novozhilov)}, volume = {5}, Number = {4}, pages = {85-93}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-350-en.html}, eprint = {http://jgst.issgeac.ir/article-1-350-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Babaee, S. S. and Mouavi, Z. and Roostaei, M.}, title = {Time Series Analysis of SAR Images Using Small Baseline Subset (SBAS) and Persistent Scatterer (PS) Approaches to Determining Subsidence Rate of Qazvin Plain}, abstract ={Monitor and detection of displacement field due to changes in land surface are one of the practical and important studies in different topics of the Geodesy, Geological and geophysical which have a significant role in trends and preventing natural disasters such as earthquake, subsidence and landslide. In the meantime, there are different methods for detection this displacement and geodetic measurement, that among them, Interferometric Synthetic Aperture Radar (InSAR) with a feature wide spatial coverage, fine spatial and time resolution and high accuracy has become one of the important and significant techniques. Land subsidence due to groundwater extraction has been a common geohazards in many arid countries and districts of the world. In Iran, this is a serious challenge for many regions, particularly in the plains with arid and semi-arid climate, for example, Mashhad valley, Hashtgerd, Yazd and Golpayegan plains. Gazvin plain is one of these regions where the land subsidence has seriously developed. This region is located in the north-central Iran, with an area size of about 4430 km2. Gazvin plain in terms of industrial, agriculture and population point is one of the important plain in Iran. In recent years, high rate of extracting water from underground source due to agricultural activity, industrial and population development caused decreasing the groundwater level, so because of the groundwater level downfall, subsidence occurs and it’s trace is seen as cracks and fractures in the ground surface. Studies show that a large area in Qazvin plain is subject to the land subsidence induced by overexploitation of groundwater for the purposes of agricultural, industrial and population development. Therefore, this research study on the pattern and rate of subsidence occurred in Qazvin plain between 2003 and 2010, using radar is Interferometric synthetic aperture radar (InSAR) techniques. The data set consists of 20 and 18 images of descending tracks D192 and D421 during 2003 to 2010. We processed radar data with the open source software StaMPS/MTI (Stanford Method of PS/Multi-Temporal InSAR). The interferograms  are corrected for the phase signature due to orbital separation using precise doris orbital data for ENVISAT satellite which is provided by DEOS are stored in a binary format (ODR) that believed to have a radial precision of 5-6 cm. then the 90 m SRTM DEM has been used to estimate the topographic phase contribution. The atmospheric phase delay in each individual interferograms corrected using the retrieved water vapors from MERIS data. We also improve signal to noise ratio of each differential interferogram using a Goldstein filter. The time series analysis of permanent scatterer (PS) and small baseline subset (SBAS) algorithms are used for deformation time series analysis. The time series results show that a considerable and continuously land subsidence in the study area. Results show a good agreement between the PS and SBAS time series, both of two approaches identify peak amplitude of ~ 30-35 mm/year for the 2003-2010 times period. With comparison width and extent of subsidence of InSAR results and plain wells density map of Qazvin plain determines the subsidence occurred in the area with the density of deep wells to extract groundwater and subsequent subsidence occurred in this area.}, Keywords = {Interferometrictime Series, Permanent Scatterers (PS), Small Baseline Subset (SBAS), Land Subsidence, Qazvin Plain}, volume = {5}, Number = {4}, pages = {95-111}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-417-en.html}, eprint = {http://jgst.issgeac.ir/article-1-417-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Sharif, M. and Alesheikh, A. A.}, title = {Similarity Measurement of Trajectories Based on Contextual Data in Constrained Euclidean Space}, abstract ={Movement of objects is taking place in geographical contexts. Context directly/indirectly influences movement process and causes different reactions to moving objects. Therefore, considering context in movement studies and the development of movement models are of vital importance. In this regard, incorporating context can play a crucial role in similarity measurement of objects movements and their corresponding trajectories. Trajectories of moving point objects, beside their spatial and temporal dimensions, have another aspect which is called contextual dimension. This dimension, however, has been less considered so far and a few researches in trajectory analysis domain have investigated it. To this end, this research develops a method based on Euclidean distance in which individual spatial, temporal, and contextual dimensions as well as their integration can be explored in the process of similarity measurement of trajectory. Beside the simplicity of the method, it is developed in a way for taking into account every small change in each type of dimension(s). To validate the proposed method and survey the role of contextual data in similarity measurement of trajectories, three experiments are performed on commercial airplane dataset. Accordingly, geographical coordinates and altitude of airplane as spatial dimension, travel time as temporal dimension, and airplane speed, wind speed, and wind direction as contextual dimension are utilized in these experiments. The first experiment measures the correspondence of trajectories in different dimensions. Also, it explores the role of dimensions weights individually and collaboratively along the similarity measure process. The results demonstrate that weights severely affect similarity values, while they are totally application dependent. Meanwhile, it can be confirmed that contexts may increase or decrease the values of trajectories similarities. This effect can be seen in the average of relative similarity values of commercial airplanes trajectories in spatial (0.60), spatial-temporal (0.51), and spatial-temporal-contextual (0.46) dimensions. Contexts can enhance and restrict movements as well. To justify this statement, the second experiment is conducted to explore how movement and geographical contexts interact in similarity measure process. To this end, four sample trajectories are compared with respect to different dimensions. For a pair of trajectory, the relative similarity value at spatial dimension is 0.04. By incorporating time dimension, this value increases to 0.30 at spatio-temporal dimension. Given the high similarity of these two trajectories in wind direction, wind speed, and airplane speed (0.85), the ultimate similarity of them becomes 0.48. In contrast, for another pair of trajectory, the spatial and spatio-temporal similarity values are 0.85 and 0.91, respectively. Considering the similarity value of these two trajectories in wind direction, wind speed, and airplane speed (0.37), the final relative similarity becomes 0.73. The third experiment sought for the role of motivation context in similarity measure process. Although such context is very difficult to capture and in many applications will remain inaccessible, we consider the pilots decisions in handling the airplanes during the approaching and landing phases (i.e., continuous descent final approach or dive and drive) as the motivation context in this application. Choosing either of these techniques highly affects the figure of trajectories where quantifying them can be accomplished by measuring the similarity of trajectories at spatial and spatial-temporal dimensions. All in all, the results of the above experiments yield the robustness of the proposed method in similarity measurement of trajectories as well as its sensitivity to slight alterations in dimensions.}, Keywords = {Movement, Trajectory, Similarity Measurement, Context, Moving Objects}, volume = {5}, Number = {4}, pages = {113-125}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-400-en.html}, eprint = {http://jgst.issgeac.ir/article-1-400-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Tehranchi, R. and MoghtasedAzar, Kh. and Nankali, H. R.}, title = {Analysis of GPS Time Series Over the Alborz Range}, abstract ={GPS time series consists of a linear trend, harmonic signals, probable offsets and also noise which is described as a stochastic part. Because of various applications of GPS time series such as plate tectonics, crustal deformation and earthquake dynamics studies, these time series should be modeled with high accuracy. For this purpose, systematic effects in functional model should be determined with high accuracy. In this paper the effect of earthquakes is also considered in the functional model in addition to mentioned behaviors. Because earthquakes cause crustal deformations, their effects can be observed in the shape of offsets (as coseismic effects) and (or) rate changes (as coseismic or postseismic effects) in the time series. Neglecting these effects lead to biased estimation of noise amplitudes. To discover the effect of earthquakes, a manual solution is used for each station. Effects are detected graphically by comparison of behavior of time series and epoch of occurred earthquakes in the region. The earthquakes which considering their effects, lead to the best fitting of functional model to time series, are selected as effective ones. Because the Alborz range is the most seismically active region in the Northern Iran, 25 permanent GPS stations with the time span between 2005 and 2013 in this area are selected for this study. Analysis of time series indicates similar behavior of time series with the same offset times and common earthquake effects for most stations (also for those which are located in far distances from epicenters). This result means that systematic effects may propagate from one station to the others during the processing and the network adjustment. Furthermore, noise analysis of time series using least squares (co)variance components estimation method, shows that neglecting seismic effects can result in the presence of random walk noise in 88%,12% and 60% of north, east and up components, respectively. However, considering the seismic effects causes positive estimation of variances of random walk noise in 12%,12% and 36% of north, east and up components, respectively. Finally, due to similar behavior of time series, a reprocessing of them could be suggested.}, Keywords = {GPS Time Series, Noise Analysis, Least Squares VCE Method}, volume = {5}, Number = {4}, pages = {127-135}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-397-en.html}, eprint = {http://jgst.issgeac.ir/article-1-397-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Emami, H. and Safari, A. and Mojaradi, B.}, title = {An Improved NDVI-Based Multivariate Regression Method for LSE Estimation on LDCM Data}, abstract ={Land surface emissivity (LSE) is an important intrinsic property of materials and knowledge of the LSE is essential to derive the land surface temperature (LST) that can be obtained from the emitted radiance measured from space. LSE provides useful information for geological and environmental studies, mineral mapping and is one of the important input parameters for climate, hydrological, ecological and biological models. The emissivity of natural surfaces inherently may vary significantly due to differences in soil structure, soil composition, organic matter, moisture content and differences in vegetation cover characteristics. In other words, LSE changes is depending on the surface (such as texture, topography, soil moisture, angular variations effect) and sensor parameters (such as spatial resolution, SRF, and effective wavelength of thermal bands). Remote sensing technology provides widely the monitoring of this quantity. Several methods exist to estimate LSE from satellite data, which apply the visible and near-infrared (VNIR) or thermal infrared (TIR) spectral regions or both of them. According to the way by which the LSE is determined along with LST, the emissivity estimation methods from optical remote sensing data can be categorized into three distinct types including, stepwise retrieval methods, simultaneous LST and LSE retrieval methods with known atmospheric parameters, and simultaneous LSEs, LST, and atmospheric quantities retrieval methods. Influential researches, in the stepwise retrieval methods, were conducted and mainly NDVI-methods have been used to predict LSE from NDVI values. In particular, NDVI-methods assume that the surface is composed of the soil and vegetation, some problems arise for other kinds of surfaces that are likely classified as bare soil pixels, such as rocks, man-made, and ice/snow. Besides, another main origin of error in these methods is caused by great changes in the emissivity of soil types. Furthermore, the choice of a typical emissivity value for some surface objects such as bare soil is a more critical question, because the variability of emissivity values for soils is more than vegetation and other ones. In this research, a new approach called improved normalized difference vegetation index-based method (INDVI_based) estimating LSE on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in bare soil, vegetation and mixed areas is accomplished based on convolving ASTER spectral Library with LDCM spectral response functions. Then, for main three areas are defined to determine separate emissivity estimate model as function of reflective bands from basic spectra associated with the main class. In the proposed method, the cannel LSEs are expressed as functions of atmospherically corrected reflectance from the LDCM visible and near-infrared channels with wavelength ranging from 0.4 to 2.29 μm fo bare soil. The effectiveness of the proposed approach was implemented in LDCM data and obtained LSE were compared and validated with two scenes of LSE standard product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results showed that LSE of the improved proposed method, in the band 10 of LDCM in comparison with the first and second LSE product of ASTER, lead to 0.76% and 0.75% errors in term of root mean square error (RMSE) measure, respectively. Moreover, this error for thermal band 11 is1.49 % and 1.06% in first and second examined scenes, respectively. Unlike previous methods, the proposed method not only accurately estimates of LSE  as a function from the reflectance of various surface objects, but also it  uses  the spectral response function  of thermal and reflective bands in estimating the LSE. In addition, the proposed method the poor relationship between LSE and only reflectance of the red band in previous methods, strengthen due to the use of all reflective bands in LSE estimation and it is applicable on most sensors.}, Keywords = {Land Surface Emissivity, Land Surface Temperature, Landsat Data Continuity Mission (LDCM), Normalized Difference Vegetation Index (NDVI)}, volume = {5}, Number = {4}, pages = {137-153}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-385-en.html}, eprint = {http://jgst.issgeac.ir/article-1-385-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Dareshiri, Sh. and Farnaghi, M.}, title = {Geoportal with Recommendation Capability}, abstract ={As the number of published geospatial resources in the web is increasing permanently, providing users with proper tools for search and discovery of these resource is of great importance. Geoportals are a type of web portals that enable users to find geospatial resources. However, when users search for a specific resource in existing geoportals, a wide range of unrelated results might be retrieved. Such results are often confusing, so that user has to spend a lot of time to find the most appropriate resource. In addition, working based on user desires and preferences has been considered as a fundamental characteristic of modern web applications. In this article, the capability of recommendation is added to the geoportal. The recommendation capability enhances the ability of geoportals which is able to recommend the most appropriate geospatial resources to users by considering their desires and preferences. In order to accomplish this purpose, a new solution with recommender systems is proposed. Recommender systems using knowledge derived from the users’ previous interactions can lead users to access resources that they are interested in. Collaborative filtering is a common technique used in recommender systems that deal with this problem. This method uses user rating data to extract the similarity between users or resources for making recommendations. Moreover, mathematical functions are defined in order to improve the efficiency of the recommender geoportal process. The functions are designed according to the specifications of users and resources such as city, county and language, along with the distance between the user and resource. In addition, an operation of these functions is to obviate the cold-start problem in collaborative filtering. To evaluate the designed geoportal recommender, a recommender geoportal is implemented so that a user is able to profit the advantages and usage of its recommendations. The obtained results indicate that the efficiency of recommender geoportals is improved compared with common geoportals.}, Keywords = {Geoportal, Recommender System, Collaborative Filtering, Geospatial Service, Geospatial Data, Cold-Start}, volume = {5}, Number = {4}, pages = {155-171}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-375-en.html}, eprint = {http://jgst.issgeac.ir/article-1-375-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Esmaeilzadeh, M. and Amini, J.}, title = {Geometric Calibration of SAR Images to Eliminate Earth’s Surface Topography Distortions}, abstract ={Geometric calibration and georeferencing are the most important processes of SAR raw images. Geometric distortions caused by platform instabilities, error in determining the relative height and displacements origin from topography. The prominent errors for the SAR imaging geometry, and target height changes are known as foreshortening and layover. Therefore, in this article our studies were focused on this problem. In order to correct these errors, an independent source of information was required such as imaging from another angle, topographic map or DEM. In this paper, a method for geometric calibration of SAR images is proposed. The method uses Range-Doppler (RD) equations and to implement the method used in this article, two SAR datasets are tested with RD modelling. These datasets are acquired by ALOS PALSAR spaceborne SAR sensor. Test areas covered by these datasets range from flat plains to mountainous areas, which the first dataset located in the border between United States and Mexico and the second one is in Iran. In this method, for the image georeferencing, the appropriate Digital Elevation Model (DEM) and also exact ephemeris data of the sensor is required. In the algorithm proposed in this paper, first digital elevation model transmit to range and azimuth direction. By applying this process, errors caused by topography such as foreshortening is removed in the transferred DEM. Then, the original image is registered to transfer DEM by transformation equations. The output is a georeferenced image without geometric distortions. The advantage of the method described in this article is in eliminating the requirement for any control point as well as the need for attitude and rotational parameters of the sensor. Furthermore, two experiments with different settings are designed and conducted to comprehensively evaluate the accuracy of the SAR georeferencing with RD model. Few experiments are done in this study for various purposes. The first one is to find the best transformation equation among the three types for registering images. In the first experiment the efficacy of three types of transformation equations on georeferencing of ALOS PALSAR images were evaluated with identified check points. To evaluate the accuracy of the georeferenced images, 25 check points in different parts of the image was selected. By comparing the obtained coordinates in georeferenced image and reference points in Google Earth, the RMSE was calculated for these points. In best situation, the planimetry accuracy were 20.11m for dataset A and 19.94m for dataset B and the altimetry accuracy were 30.28m for dataset A and 30.71m for dataset B. Since the ground resolution of multi-look image was 30 meters, the planimetry accuracy achieved in this research is acceptable. The other experiment is to compare the georeferenced SAR images generated from three DEMs to demonstrate the effectiveness of DEM spatial resolution on the accuracy of georeferencing SAR images. In addition we investigated the suitability of three typical DEM datasets for SAR georeferencing in RD model. The experimental results show that the best transferred DEM was obtained from the ASTER DEM of spatial resolution comparable to that of ALOS PALSAR images.}, Keywords = {Georeferencing, Foreshortening, Layover, Shadow, Range-Doppler, Digital Elevation Model, Transformation Equations}, volume = {5}, Number = {4}, pages = {173-185}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-370-en.html}, eprint = {http://jgst.issgeac.ir/article-1-370-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Ghadimi, A. and MoghtasedAzar, Kh. and Nankali, H. R.}, title = {A Study on Crustal Deformation Analysis of the Alborz Region : GPS Versus Seismological Observations}, abstract ={The Alborz range of northern Iran is a region of active deformation within the broad Arabia–Eurasia collision zone. The range is also an excellent example of coeval strike-slip and compressional deformation, and as such can be an analogue for inactive fold and thrust belts thought to involve a component of oblique shortening. It is roughly 600 km long and 100 km across, running along the southern side of the Caspian Sea. Several summits are 4000 m in altitude. Damavand, a dormant volcano, reaches 5671 m. The highest non-volcanic summit is Alam Kuh, at 4830 m. The Alborz range, northern Iran, deforms by strain partitioning of oblique shortening onto range-parallel left-lateral strike-slip and thrust faults. Deformation is due to the north–south Arabia–Eurasia convergence, and westward motion of the adjacent South Caspian relative to Iran. The occurrence of moderate to large earthquakes in the Alborz suggests an important deformation regime in this mountain belt. This belt has been responsible for several catastrophic earthquakes in the past. The Manjil earthquake of 20 June 1990, which is the most disastrous Iranian earthquake in the twentieth century, occurred in this belt. Both thrust and strike-slip faulting have been reported in this belt. By the knowledge of the crustal deformation characteristics in areas with active tectonics we can realize the style, direction and magnitude of the deformation in the area, it can contribute to the deeper understanding of the underlying tectonic processes and to the improvement of the seismic hazard assessment. In this contribution strain rate fields (using geodetic data vs. seismic data)  calculated over the three different parts of Alborz regions (Western, Central, and Eastern Alborz). Eigenspace components of seismic strain tensor (seismic events with Ms ≥ 4.0 in the time interval 1900–2010) over three zones revealed crustal shortening over all three zones. Namely, the results of seismic data showed left-lateral strike-slip faulting in the Eastern Alborz, the right-lateral strike-slip motion in the Western Alborz and compression mechanism in the Central zone. The highest compressional rate in the Western Alborz probably represent the high seismicity rate of this region. In comparison, eigenspace components of geodetic strain tensor (during time interval from 2005 to 2009) illustrated high rate of compressional components in the Central and Western zones. Comparison of seismic and geodetic strain rates in the Western Alborz indicates the deformation rate over this region is associated with seismic activities. However, in Central and Western regions the geodetic strain rates appeared remarkably larger than seismic strain rates. May, this fact illustrated the deformation pattern in these regions are related to the local aseismic creep of those segments. The difference between eigendirection of both kind of tensors (geodestic vs. seismic) in segments probably were related to the short period of GPS data with respect to the seismic data.  However, it is a fact that early historical data are incomplete. So, the catalog completeness is crucial for estimating reliable seismicity strain rates and, consequently, for use in seismic hazard assessments. Hence, the performing the repeated geodetic data over the Alborz region is proposed to investigate the reliable estimating of the strain rate.}, Keywords = {Seismic Strain Rate, Geodetic Strain Rate, Maximum Compression and Maximum Extension Axis, Seismic Catalog}, volume = {5}, Number = {4}, pages = {187-198}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-347-en.html}, eprint = {http://jgst.issgeac.ir/article-1-347-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Izakian, Z. and Amerian, Y. and MesgariSaadi, M.}, title = {Time Series Data Clustering Based on Differential Evolution Algorithm and Discrete Cosine Transform}, abstract ={Time series is a type of data with complex structure. Analysis of time series is used in sciences such as meteorology, economics, geology, marine science, medicine and engineering widely. So, Because of time series applications in various sciences, the interest to analyze these data has been increased.On the other hand by developing information gathering technologies such as mobile, GPS and sensors, and Access to large volumes of time series data, we always require methods to extract useful information from large datasets. Thus, data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Clustering is a strong instrument for knowledge discovery and it provides useful information about existing patterns in datasets. In general, the purpose of clustering is representing large datasets by a fewer number of cluster centers. It simplifies large datasets and thus is an important step in the process of knowledge discovery and data mining. Fuzzy C-means (FCM) clustering is one of the most important classic clustering methods that have been used in many researches. The main disadvantage of this method is the high probability of getting trapped in local optima especially in facing high-dimensional data such as time series. Furthermore Euclidean distance is the most commonly used similarity measure in Fuzzy C-means but sometimes, its necessary to use another similarity/dissimilarity measures instead of Euclidean distance. In this paper in order to compensate the shortcomings of Fuzzy C-means algorithm, we used one of the existing evolutionary algorithms. Evolutionary algorithms has gained huge popularity in the field of pattern recognition and clustering recently. Among the existing evolutionary algorithms, the differential evolution algorithm as a strong, fast and efficient global search method has been attracted the attention of researchers. In this paper, we proposed a technique for clustering time series data using a combination of Fuzzy C-means and differential evolution (DE) approach and we considered dynamic time warping (DTW) as distance measures between time series. Also, in this method we used Discrete Cosine Transform (DCT) to time series dimension reduction. Finding all elements of cluster centers using differential evolution is time consuming and the large number of unknown parameters related to the cluster centers will reduce the efficiency and the speed of differential evolution algorithm.So, for reducing the search space,the most important Discrete Cosine Transform coefficients of the cluster centers were recognized as the main unknown clustering problem in the proposed method and differential evolution algorithm tries to determine the near optimal Discrete Cosine Transform  coefficients of cluster centers by minimizing the Fuzzy C-means objective function. Experimental results over two popular data sets indicate the superiority of the proposed technique compared to fuzzy C-means and a clustering algorithm based on differential evolution without using dimension reduction techniques.Comparing the run time of the methods, the proposed method is slower than the Fuzzy C-means clustering algorithm, but due to the use of discrete cosine transform method to reduce unknowns, it operates faster than differential evolution without using dimension reduction techniques.}, Keywords = {Time Series, Clustering, Fuzzy C-Means, Differential Evolution, Discrete Cosine Transform}, volume = {5}, Number = {4}, pages = {199-209}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-341-en.html}, eprint = {http://jgst.issgeac.ir/article-1-341-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Gharibi, M. and Arefi, H. and Rastiveis, H. and Hashemi, H.}, title = {Building Map Updating Based on Active Contour Models}, abstract ={The rapid growth and development of urban environments has been created a lot of motivation for researchers in Geomatics engineering in order to provide optimal methods for monitoring urban changes and updating maps. Nowadays, using aerial/satellite imagery for updating old maps is one of the important topics in Photogrammetry and Remote Sensing. In this case, the available information in the digital cartographic data can be used as training data for classification, creating conceptual model, reducing the search space and also to estimate the unknown parameters like segmentation parameters. Applying available information of cartographic data leads to contribution this type of information during the feature extraction in order to improve the efficiency and decrease the defects of this progress. Therefore, this paper proposes a novel approach for building extraction in order to building map updating from aerial images with help of old digital cartographic data. In this study, the geometric information of polygons existing in old cartographic data is used as an auxiliary data to improve the process of building extraction and change detection based on active contour models in a hierarchical approach. The building extraction process is done in two step using two types of active contour models which runs upon the height and spectral data. The active contour models in the face with large dimensions, high level of details and also images with weak gradient information have not acceptable performance. Therefore, the focus of this paper is to present a novel approach to compensate the above mentioned defect. So, the building extraction process is done in a hierarchical approach based on a combination of two geometric active contour models which causes the elimination of the defects of these models in the extraction of buildings with different geometric and spectral behaviors. In the proposed method, each of the polygons are considered as an initial curve to a region-based geometric active contour model. This model is run upon a part of the DSM, commensurate with the position and dimension of the old polygon. After primary extraction of the building boundaries from the DSM, geometric change detection is done and then, the change map is produced. The change map gives a comprehensive intuition about the occurred changes. Due to various errors in DSM, the primary extracted boundaries did not have sufficient accuracy. So, to improve the accuracy of these boundaries, the results are introduced to a constrained edge-based geometric active contour model which is one of the innovations of this study. The common edge-based active contour models cannot recognize the object boundary in images with weak gradient information and so, the level set function evolution well be unstable and therefore will not be getting correct results. To solve this problem, a novel approach as constrained level set formulation is proposed. This constraint is derived from output of the region-based model and is caused to solve the deficiency of this model in the face with weak gradient images. After the extraction of the precise boundaries, the MBR-based approach as an approximation or generalization technique is applied for irregular generated boundaries of changed buildings which are obtained from the proposed constrained edge-based model. Finally, the generalized polygons are record in geodatabase. The dataset used in this study concern the part of vaihingen city of Germany. The shape accuracy of extracted buildings and overall accuracy of change detection process were 92% and 78%, respectively. These results clearly demonstrated the success of the proposed method in building extraction using active contour models and change detection in order to automatic building map updating.}, Keywords = {Updating Building Plan, Revealing the Geometric Changes and Update the Map, Active Curve Models, MBR}, volume = {5}, Number = {4}, pages = {211-225}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-312-en.html}, eprint = {http://jgst.issgeac.ir/article-1-312-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Karami, J. and Delfan, S. and Shamsoddini, A.}, title = {Role of Time in Spatial Analysis of Diseases in Tehran}, abstract ={Changes in socio-economic trends and factors that affect on the health of a society as well as changes of environmental  that occur over time, can alter the effects of a disease at different times. At the level of city, Identify regions with high risk of getting a particular disease that can be lead to death, Compared to other regions and investigate the behavior of any of these diseases during different time periods, an important role in disease control and health management in the community. In this study, investigated the trends of disease-based mortality in different regions of Tehran  with revolving around seasonal and annual changes during the period 1993-2013. Geostatistical analysis  using  different tools to assess the relationship between disease at a certain time and in one place with the surrounding area. The Local Moran's I index is Including neighborhood analysis tools that to assess disease in  specific time and place are effective to neighboring regions and find the neighborhood at different times, to identified regions of high risk (HOT SPOT). Moran index value articulated the relationship of a variable and its distribution in space and time with neighboring regions. diseases of Hot Spots  and classification them in spatial clusters on the basis of this index, can be identified. Neighborhood is defined in such a way that at the level of a city is different for various diseases. This optimum amount neighborhood may be affected at different time intervals depending on the nature of the disease and also affected it is   different from local factors or global. Results of the analysis on 12 group’s mortality disease-based showed that spatial significant levels are different in each disease. The relationship between diseases in different regions may be clustered in regions or dispersion they exist. This amounts to 5 groups of diseases, including diseases of the brain, liver, gastrointestinal  and bleeding, cancer and stomach cancer have a meaningful relationship with the spatial factor’s in Scaling throughout a city. It is worth mentioning that This relationship may be different in larger scale than the area of the  city. In the next stage, using temporal of  analysis  for five groups of diseases that have higher spatial index, Amount dependence of any of the diseases during the  time periods of  annual and also examined changes of any disease in different  season  . The results showed is  different neighborhood radius for different diseases in different seasons. Also regions with a high risk of each disease being different with the change of seasons and changing environmental parameters. In examined the results of annual trends showed that during twenty year period, Amount dependence of any of the diseases to locations due to changes In socio-economic status and health of the community is quite different.}, Keywords = {Spatial-Temporal Analysis of Diseases, Local Moran's I Index, Relationship Between Time with the Mortality, Hot Spots}, volume = {5}, Number = {4}, pages = {227-238}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-394-en.html}, eprint = {http://jgst.issgeac.ir/article-1-394-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Moradi, M. and Delavar, M. R. and Moradi, A.}, title = {Design and Implementation of a Spatial Decision Support System for Land Use Suitability Assessment Based on Sugeno Integral and Imperialist Competitive Algorithm}, abstract ={Land use suitability assessment is a traditional problem and many researches have been undertaken to address this problem. The main reason why this problem is important is that experts want to consider all the aspects into account when they are trying to find the optimum location for a specific purpose. In other words, experts want to find the best place from all point of views such as environmental, ecological, economic and political aspects. Therefore, a decision support system is obligatory in order to facilitate decision making using mathematical models. On the other hand, due to the fact that a place is going to be chosen in this problem, GIS is a main science involved in this assessment. In this paper, a spatial decision support system is proposed using the integration of Sugeno integral and Imperialist Competitive Algorithm (ICA). Sugeno integral is able to aggregate alternative scores with respect to their interaction. In other decision making methods, it is assumed that the criteria are independent but it is against the real world situations. For example, in land use suitability assessment problem, some criteria such as land price and distance to major roads are not independent. Therefore, this study can improve spatial decision support systems by taking the impact of interaction among criteria into account. Sugeno integral operator uses fuzzy capacities instead of layer weights. Fuzzy capacities show the importance of each group of criteria for land suitability assessment. Furthermore, Sugeno integral can provide a number of numerical measures to indicate the importance of each criteria (Shapley value), the interaction among each set of criteria (interaction index) and the power of each criteria to veto the final decision (veto index). Shapley value is a parameter defined by game theory which indicates the power of each player in a game. In terms of decision making problem, Shapley index shows the importance of each criterion in the decision making process. A more important criterion has a higher impact on the results of the decision making. Interaction index shows how two players cooperate. If the two players have a positive cooperation they will make a better situation and if they have negative interaction the power of their coalition will be less than the power of each of them. In multiple criteria decision making, interaction index represents how two criteria interact. When the simultaneous satisfaction of two criteria is favourable, the interaction among them is positive and when the simultaneous satisfaction of the two criteria is not what the decision maker wants, it means that the two criteria have negative interaction. In this research imperialist competitive algorithm is applied to find the best values of fuzzy capacities that best describe the experts’ knowledge. In other words, a constrained optimization problem is solved here to compute the optimum value of fuzzy capacity for each set of criteria. ICA is selected because it is able to find the optimum value of a continuous function under constrains. The proposed SDSS is employed for land use suitability assessment for a new power plant. The results indicate that the method is highly suitable for modeling GIS-based decision making over interacting criteria. This model may be used in other areas of decision support systems with minor modifications.}, Keywords = {Spatial Multiple Criteria Decision Making, Fuzzy Integral Operator, Game Theory, Optimization Algorithm Competition Colonial Power Plants Wind, Assess the Suitability}, volume = {5}, Number = {4}, pages = {239-253}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-317-en.html}, eprint = {http://jgst.issgeac.ir/article-1-317-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {MalekpourGolsefidi, M. and Karimipour, F. and Sharifi, M. A.}, title = {Proposing a Novel Temporal Rout Finding Model for Marine Navigation with Respect to Depth and Weather Condition of Marine Environment}, abstract ={Nowadays, considering the importance of marine commerce, monitoring the marine navigation and routing the ships could be regarded as important issues. Moreover, specifying the weather condition of the marine environment for minimizing the damages and fatalities to vessels, crews and cargos is vital. Hence, weather routing is absolutely crucial. In addition, due to the high cost of voyage, the duration of voyage is one of the essential parameters of weather routing. The aim of this research is to minimize the voyage duration regarding the weather conditions. The marine environment is simulated by a grid of weather data which has the resolution of 0.25 D that is updated every 6 hours. The data is downloaded from European Centre for Medium Range Weather Forecasts (ECMWF). Following that, the weight of each edge is calculated with respect to the time in which the vessel passes the edge.  Travel time is related to the impact of wave, wind and sea depth on vessel’s speed which is computed based on Kwon method and Lackenby’s formula. Finally Dijkestra algorithm is applied for calculating the optimum route. The studied area located in the north of Indian ocean in Persian gulf, Oman Sea and Arabian Sea. The model is implemented in two different weather conditions (calm and rough conditions) for calculating minimum time route between Pipavav port ( ) in India and Bushehr port ( ). The results indicate that although the voyage distance increased in this model, the duration of voyage decreased. Thus, the cost of voyage dropped noticeably. In addition, the depth of the marine environment determines the route of journey in the calm weather conditions because of lack of existence of high seas and storm in front of the ship. In the rough weather conditions the weather condition parameter (speed and direction of the wind and height and direction of the wave) has more effect than depth parameter in order to prevent the ship from navigation in high seas in which the ship's speed reduces dramatically. Moreover, results show that in the bounded seas like Persian Gulf with small area with respect to spatial resolution of marine environment (the resolution of the weather data) in which the weather condition between neighbouring cells does not change obviously, depth parameter is the critical parameter to determine the journeys path.}, Keywords = {Routing, Optimization, Dijkstra\'s Algorithm, Navigation, Kwon Method}, volume = {5}, Number = {4}, pages = {255-268}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-418-en.html}, eprint = {http://jgst.issgeac.ir/article-1-418-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} } @article{ author = {Abbasi, O. R. and Alesheikh, A. A. and Karimi, M.}, title = {Using a Hybrid Semantic Similarity Assessment Model to Resolve Semantic Heterogeneities in SDIs Case Study: Iranian Water and Wastewater Company}, abstract ={Many countries aim to design and build Spatial Data Infrastructure (SDI) to facilitate, manage and share spatial data. Different public or private organizations provide data sources in diverse ways and various contextual situations such as weather conditions, coordinate system definitions or acquisition times. Therefore, SDI should be semantic-based, as possible as it can, to deal with different user languages, requirements. Such an SDI can help providing appropriate representation and search. Since the data integration is an essential part of each information system, semantic similarity is getting more attention in the web world. An efficient spatial data sharing across different organizations is considered to have significant contributions to the sustainable development of today’s communities. As the quantity and accessibility of spatial data is tremendously increasing via web, interpreting, handling and retrieving of this data has become a difficult task. The data suppliers come from various information communities with differing conceptualizations of the world. So, this data is heterogeneous in essence and distributed over several sources. Since the acquisition of geospatial data is extremely expensive, developing mechanisms for reusing and sharing geographic information are necessary to save costs. Besides, customer orientation and personalization of data sources is central to enable flexible and multipurpose usage of the data and to provide customers with the required data. Ordinary information retrieval systems are limited to syntactic retrieval mechanisms and therefore cannot deal with semantic differences in the customer's and the data supplier's conceptualization. The Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information but these standards cannot solve the semantic problem. Today, the semantic heterogeneity is considered as the main obstacle to the full interoperability among spatial data sources. Geospatial  data  describes  real  world  geographic  features  by  their  spatial  extent  and their location. Hence, properties are necessary to capture the semantics underlying geospatial data, because they can represent spatial qualities such as shape. The notion of semantic similarity serves as an indicator for relevance in the retrieval process. This paper uses an ontology-based approach and description logic to resolve the semantic heterogeneity. For this purpose, semantic similarity measurement is used to interpret, handle, and retrieve data in terms of semantically similar concepts. In order to calculate similarities, two existing similarity measurement models were combined: Feature model and Network model. While Feature model computes similarity of concepts based on their common and distinctive properties, Network model puts the concepts in a semantic network and computes the similarity based on the relations of the concepts in the network. This paper proposes a hybrid similarity model as a computational model for semantic similarity measurement. This hybrid model enables the necessary expressiveness to capture semantics underlying geospatial data. The shortcomings and benefits of each model with respect to the requirements of semantic information retrieval of geospatial data are described. Retrieval systems use similarity measures to determine the relevance. Only a retrieval system which returns cognitively adequate results can successfully support human users. The proposed model retrieves relevant information by measuring the semantic similarity of concepts to a given query. The methodology has been tested on some parts of Iranian Water and Wastewater Company’s infrastructure as a case study. Since semantic similarity is an appropriate means to resolve semantic heterogeneity in retrieving data in SDIs, the proposed model can help users by representing similarity in a quantitatively manner. This paper has considered blockage in pipeline as user search concept. The results of similarity represent the advantages of the proposed model. In addition, the results showed that the most similar concept to user search concept was Elbow with %42.5 similarity because of its curvature.}, Keywords = {Spatial Data Infrastructure (SDI), Semantic Similarity Assessment, Ontology, Description Logic (DL), Geospatial Information System (GIS)}, volume = {5}, Number = {4}, pages = {269-280}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-389-en.html}, eprint = {http://jgst.issgeac.ir/article-1-389-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2016} }