Region-Merging Method With Texture Pattern Attention for SAR Image Segmentation

Abstract

This letter proposes a region-merging-based method for synthetic aperture radar (SAR) image segmentation, where the merging cost is a fusion of texture pattern similarity measure (TPSM), statistical similarity measure (SSM), and the relative common boundary length penalty (RCBLP). The segmentation is implemented in three steps. First, an image is oversegmented based on the multiscale Bhattacharyya distance to generate an initial partition of considerable regions. Second, regions with sizes under a given threshold are mandatorily merged to yield a middle segmentation. Third, a region-merging process using the new merging cost is iteratively conducted to achieve final segmentation. Due to the existence of the TPSM in the merging cost, the new method avoids the false merging of adjacent regions with different textures. Experimental results of the real SAR images show that the proposed method outperforms existing region-merging-based methods.