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.

This paper proposes a novel merging cost with texture pattern discrimination for region merging in the segmentation of SAR images, which integrates texture patterns, statistical characteristics, and shape prior of SAR images. By the initial partition based on multiscale Bhattacharyya distance, the input image is first over-segmented into region patches. Then, in the region merging process, a correlation measure of texture patterns between adjacent patches is constructed to obtain the proposed merging cost by fusing it with statistical similarity measure (SSM) and the relative common boundary length penalty (RCBLP) according to the pixel number of region patches. Finally, the segmentation result is output by iteratively merging the pair of adjacent regions with the smallest merging cost until the end condition is satisfied. Experimental results on real SAR images indicate that the proposed method is competitive in the segmentation of SAR images with complex scenes in comparison with several recent state-of-the-art ones.

In this paper, a novel edge detector for synthetic aperture radar (SAR) images is proposed by introducing the Bhattacharyya coefficient (BC) combining with the rotated biwindow configuration. Based on the quantified input image, the BC is computed from two sample distribution histograms of local regions supported by the subwindows on the opposite sides of the pixel to be detected. With biwindows of different directions sliding through the image, multiple directional Bhattacharyya coefficient matrices are obtained, which are utilized to extract the edge strength map (ESM), characterizing the intensity variation in SAR images. Sequent nonmaximum suppression and hysteresis thresholding refine the extracted ESM into thin edges. Experiment results show that the proposed edge detector can accurately extract edges. Moreover, the BC-based ESM can act as a good precursor to guide SAR image segmentation based on region merging.