@article{ author = {Mohammadzadeshadmehri, M. and Sharifi, M. A. and EbrahimzadeArdestani, V. and Safari, A. and Baghani, A.}, title = {Inverse Modeling of Gravity Data Using Information Resource Border Anomaly}, abstract ={Abrupt changes in gravity data anomaly is edges. Edge detection methods in gravity data can be found in the basement of the location and boundaries of a mass crime. The purpose of this study, Improvement of uniqueness and avoiding premature convergence in inverse modeling of gravity data using boundary position information resources. To evaluate the proposed method, firstly gravimetric data modeling with irregular geometry stepped on a synthetic model were implemented without edge information. In the second phase edge information in inverse modeling gravimetric data were used artificial model. The results showed that using these constraints, we were able to limit the search space, As a result ant colony optimization algorithm for solving this problem increased and less time to reach a reliable conclusion. Finally the method on data Gotvand area were also implemented.}, Keywords = {Gravity Data, Inverse Modeling, Ant Colony Algorithm, Edge Extraction}, volume = {0}, Number = {0}, pages = {1-9}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-601-en.html}, eprint = {http://jgst.issgeac.ir/article-1-601-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} } @article{ author = {Mohammadi, R. and Farnaghi, M.}, title = {Road Geometry Extraction from Trajectory Data using Crawler Agents based on KDE}, abstract ={Volunteered Geographic Information (VGI) has been recently used to generate low cost, up-to-date, and reliable road maps. Previous methods are periodically applied to update the road maps in a particular time interval and scan the whole study area instead of considering the changed parts of road networks. This paper presents a multi-agent system that dynamically explores the newly collected trajectory data and updates the Google Earth’s road map geometry. In this regard, the developed agents use morphological operation in a raster space, generated from newly added trajectory data to extract road map. Kernel Density Estimation (KDE) method was exploited to transform vector space of trajectory data to raster. Moreover, the road geometry extraction algorithm is applied on the changed parts, reported by newly added trajectory data, instead of the whole study area. In this regard, the work space is divided into regions of equal size. To evaluate the functionality of the proposed algorithm, the result was compared to the Google Earth’s base-map and F-score, Missing and Spurious parameters were 0.57, 0.40 and 0.37, respectively.}, Keywords = {Agent, Crawler-Agent, Automatic Road Extraction, Trajectory Data, VGI, KDE}, volume = {0}, Number = {0}, pages = {11-21}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-602-en.html}, eprint = {http://jgst.issgeac.ir/article-1-602-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} } @article{ author = {Teimouri, M. and Mokhtarzade, M. and ValadanZouj, M. J.}, title = {A Fusion Approach for Building Detection from High-Resolution SAR Image in Urban Area}, abstract ={Precise and up-to-date spatial information from natural and artificial phenomena are needed for better management of urban and semi-urban areas. For this purpose, extraction of spatial information from buildings, as one of the dominant urban features, has attracted the attention of photogrammetry and remote sensing specialists. Thus achieving an appropriate algorithm for detection and extraction of buildings from aerial and satellite images is crucially important. In this paper, therefore, it has been attempted to propose a method for improving detection of buildings in TerraSAR-X images. In the proposed method, applying integration of classifiers and employing neighbourhood information of each pixel have improved the results of building detection and reduced the amount of noise. Therefore, in this method, initially extraction and selection of the optimum feature is carried out. Then, the capability of various classifiers like Neural Network, Support Vector Machine, Maximum Likelihood and Nearest Neighbour is assessed and compared. Then, for improving the results, the results are integrated in decision level using a moving window. The results of the proposed method with overall accuracy and building detection accuracy of 86.41% and 73.08%, respectively indicates the capability of this method. Also, the proposed integration method has improved the results at least by 5% compared to that of individual usage of each classifier.}, Keywords = {Building Detection, Fusion, SAR, Neural Network, Support Vector Machine, Nearest Neighbour, Maximum Likelihood}, volume = {0}, Number = {0}, pages = {23-32}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-603-en.html}, eprint = {http://jgst.issgeac.ir/article-1-603-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} } @article{ author = {MohammadzadeZangalani, T. and TavakkoliSabour, M. and Riyahibakhtyari, H. R. and Sadeghi, H. and Hoseinypoor, A.}, title = {Performance Evaluation of Speckle Noise Reduction Methods in Spatial Domain and Wavelet}, abstract ={In other to use radar images speckle removing or reduction is necessary. Speckle reduction methods have difference performance according to the aim of use. In this paper we implemented Speckle reduction methods on the image in wavelet and spatial domain. For performance evaluated we used RMSE and SNR indexes for test image and SSI indexes for real image. According to the results, wavelet domain methods performance are better than spatial filters. Among the wavelet filters,  2-D Double-Density and Dual-Tree Complex had better performance in total. In evaluated real image performance with SSI index seen a slight difference with test images results. Therefore it is better that SSI index to be used with catious due to the small homogeneous regions in image and compared with test image data and visual interpretation.}, Keywords = {Speckle, Wavelet, Spatial Filters}, volume = {0}, Number = {0}, pages = {33-39}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-604-en.html}, eprint = {http://jgst.issgeac.ir/article-1-604-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} } @article{ author = {Hassanlou, M. and AhmadiSalianeh, S. H.}, title = {Object Extraction from the WorldView-3 Sattelite Imagery Using Adaboost Algorithm with Haar-Like Features}, abstract ={Progress and development of any society depends on having accurate information from its environment. This information extraction can be achieved in several ways. One of the ways you can manually extract the position of the objects from satellite images that very time consuming. Automatic extraction of the features from satellite imagery has provided sufficient accuracy can be considered as an appropriate method to replace manual methods. This paper addresses Adaboost training algorithm and its sensitivity analysis by using Haar-like features. As the algorithm’s name imply, a set of images are required to train the algorithm and to build positive and negative training schemas. The images are extracted form three features such as automobile, airplane and oil reservoirs; moreover the images are utilized for the training of the algorithm. After the algorithm’s training for each aforementioned features by using enough quantity of schemas, the training procedure is completed and ready for the feature extraction from the satellite images. This research have studied on WorldView-3 satellite images with the spatial resolution of 40 centimeters. In present study and after the feature extraction, the algorithm’s results are compared to results which is derived from the manual extraction. The comparison shows the high efficiency and precision of the algorithm. In the automobile and oil reservoirs accuracy and completeness algorithms able to reach 90percent. The main characteristic of this algorithm is to run quickly in very large high resulation satellite imagery. In order to analyze the sensitivity of the, the interdependency of the algorithm’s precision and completeness is studied in comparison with a verity of parameters of algoritm’s including the number of training stage’s and completeness of each stage so on that can be very expediting and helpful in the training of the algorithm for the extraction of other features.}, Keywords = {Feature Extraction, Adaboost, Haar-like, Training}, volume = {0}, Number = {0}, pages = {41-53}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-605-en.html}, eprint = {http://jgst.issgeac.ir/article-1-605-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} } @article{ author = {KiavarzMoghaddam, M.}, title = {Land Surface Thermal Anomaly Detection Based on Satellite Thermal Band Nomalization}, abstract ={Thermal remote sensing is a useful and economic method for geothermal exploration. Thermal remote sensing data used to map and quantify temperature anomalies associated with surface geothermal features such as hot springs, fumaroles, and heated ground. Solar, lapse rate and evapotranspiration effects has been modeled to reduce the non-geothermal effects on land surface temperature. A least square methodology has been applied to calculate the model coefficients for Landsat satellite images. The final thermal anomaly map was compared exist thermal anomaly features. The results show 63% of calculated anomaly regions were consistent with real anomaly features.}, Keywords = {Thermal Remote Sensing, Thermal Anomaly, Geothermal}, volume = {0}, Number = {0}, pages = {55-65}, publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران}, url = {http://jgst.issgeac.ir/article-1-606-en.html}, eprint = {http://jgst.issgeac.ir/article-1-606-en.pdf}, journal = {Journal of Geomatics Science and Technology}, issn = {2322-102X}, eissn = {}, year = {2017} }