Minimum Distance Texture Classification of SAR Images in Contourlet Domain

Contourlet has shown good performance in different aspects of image processing. Because of its charming multi-resolution and multi-direction characteristics, Contourlet is especially efficient in the processing of images with abundant texture. In this paper, a texture classification method is presented for the SAR images based on the Contourlet transform and a minimum distance classifier. The interested image is firstly decomposed with Contourlet transform. Then, the texture feature vector is constructed with only the significant first order statistical features of Contourlet coefficients for the concern of computational complexity. In the classification procedure, the Euclidean distance is used as the measurement of the texture similarity between two feature vectors. Finally, the minimum distance classifier is applied for the classification of the target images. The proposed texture classification method is implemented with the SAR image of the Cairo city. Numerical results show that the proposed method has good performance for the classification of areas with distinctive texture features.