SAR Image Segmentation Using Contourlet and Support Vector Machine

In this paper, we propose a SAR image texture segmentation method based on Contourlet transform and Support Vector Machine (SVM). The Contourlet transform provides a multi-resolution and multidirectional representation of images, which can capture the directional texture information into subbands of different scales and directions. Thus, the energy, standard deviation and information entropy defined on Contourlet subbands are used to construct the texture feature vector. The SVM is performed as a nonlinear classifier, and the decision tree strategy is applied to extend the primitive binary SVM to a multiclass SVM. Numerical results show that all three interested textures are successfully segmented.