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

• Published in: IEEE geoscience and remote sensing letters Vol. 18; no. 1; pp. 112 - 116
• Main Authors: Fan, Shuchen, Sun, Yuhe, Shui, Penglang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Gray-scale; Image processing; Image segmentation; Length measurement; Merging; Merging cost; multiscale Bhattacharyya distance; Radar imaging; Radar polarimetry; Regions; SAR (radar); SAR image; Self-similarity; Similarity; Similarity measures; Synthetic aperture radar; synthetic aperture radar (SAR) image segmentation; Texture; texture pattern similarity measure (TPSM); Transforms
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.2969321

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.