Monitoring and modeling urbanization require reliable analytical techniques and suitable methods of visualization. However, it presents unique challenges due to the spatial and spectral heterogeneity of the urban surfaces and the rapid changes in land cover that occur over short time periods. Remote sensing methods have been successfully used for various issues such as environmental, urban growth etc…, especially in areas where only insufficient field data are available for urban development and management. Satellite images were used to perform the land cover classification using the traditional methods such as Unsupervised and Supervised (Pixel based) classification and Segmentation (Object based) approaches. Classifying the complex structures of urban morphology from high resolution remote sensing imagery comprises difficulties due to their spectral and spatial heterogeneity. This Paper presents a methodology allowing to derive meaningful area-wide spatial information for city development and management from high resolution imagery. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology.
Object oriented, classification, segmentation, spatial information, accuracy assessment, Urban morphology
