Fusion Segmentation Algorithm for SAR Images Based on the Persistence and Clustering in the Contourlet Domain

In view of the speckle noise in the SAR images, utilizing the Contourlet´s advantages of multiscale, localization, directionality and anisotropy, a new SAR image fusion segmentation algorithm based on the persistence and clustering in the Contourlet domain is proposed in this paper. The algorithm captures the persistence and clustering of the Contourlet transform, which is modeled by HMT and MRF, respectively. Then, these two models are fused by fuzzy logic, resulting in a Contourlet domain HMT-MRF fusion model. Finally, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis indicate that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.