Nowadays spatial data and urban areas rapidly changing due to the many kinds of natural and artificial factors. These changes lead to the loss of reliability of the information for urban planning, resource management and inefficiency of spatial information systems. so, monitoring of these changes and obtaining update information about the land use and the kind of its changes is essential for urban planning, proper resource management, damage determination assessment and the updating of geospatial information systems. Therefore, more accurate change detection is a challenge for experts and researchers of remote sensing and photogrammetry. In recent years, various techniques have been developed for change detection especially on high-resolution images that choosing the appropriate method and algorithm to identify changes is not easy. Despite all the efforts of researchers to develop different methods for change detection, all techniques and methods have advantages and limitations. This article introduces a new category of changes detection methods. In general, methods and techniques of change detection in urban areas can be categorized into four major categories: direct comparison- post classification dipole, object based- pixel based dipole, supervised- unsupervised dipole, textural and spatial information and features. Despite a rich and useful spectral information in high-resolution satellite images of remote sensing and photogrammetry, just use of this kind of information, will not be enough to achieve the required accuracy due to increased variability within homogenous land-cover classes. So, in this paper, in addition to the spectral features, it is also used texture features extracted from the spatial and frequency domain (Spectral, Anomaly, Edge, Morphological building index (MBI), Other color space, Gray Level Co-occurrence Matrix (GLCM), Features extracted from wavelet transform, Features extracted from Gabor filter, Features extracted from Fourier transform and Features extracted from curvelet transform) to solving this problem and generating changes mask of high-resolution images. The diversity and variety of extracted features from the spatial and frequency domain require optimization algorithms to achieve optimum features. Therefore, particle swarm optimization and genetic algorithms have been used to achieve optimum features and optimum parameters of support vector machine simultaneously. Also according to the major weakness of post classification method for detection of intra-class changes and bad radiometric conditions of used images for segmentation, 2-class classification of differential features is used to detect changes. QuickBird (0.6 m -  October 2006) and GeoEye (0.5 m - August 2010) satellite imagery of AzadShahr/Tehran/Iran are used to evaluate the proposed method. The overall accuracy 93.45 and kappa coefficient 0.87 versus 91.03 and 0.82 show that particle swarm optimization is better than a genetic algorithm to achieve optimum features and optimum parameters of support vector machine simultaneously. It also calculates the effectiveness of each 10 kinds of features used by three criteria introduced in this paper (Effectiveness, Minor Effectiveness, and Overall Effectiveness), indicates the efficiency of using other color spaces, features extracted from wavelet and features extracted from spatial domain (Gray Level Co-occurrence Matrix) and also reflects the weakness of using only spectral data to detect changes in high-resolution images. Compare the proposed approach with other studies (post classification and fuzzy thresholding method) show the effectiveness of proposed method.