Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification

Many cities in developing countries are facing rapid growth of dynamic slum areas but often lack detailed information and analysis on these informal settlements. Multiresolution analysis (MRA) has been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a texture-based segmentation scheme using newly developed multiresolution methods for slum area identification. The proposed method is tested on remotely sensed images where textural information in terms of statistical moments and energy are extracted at various scales and in different directions with the help of curvelet and contourlet transforms. The results are compared with wavelet-based MRA method of segmentation. Accuracy assessment is performed for segmented images, and comparative analysis is carried out in terms of class-wise and overall accuracies. It is found that the proposed method shows better class-discriminating power as compared to existing methods and overall classification accuracy of 91.4-95.4%.