A Gamma Distribution-Based Fuzzy Clustering Approach for Large Area SAR Image Segmentation

• Published in: IEEE geoscience and remote sensing letters Vol. 18; no. 11; pp. 1986 - 1990
• Main Authors: Zhao, Xuemei, Wang, Haijian, Wu, Jun, Peng, Zhiyong, Li, Xiaoli
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Clustering; Clustering algorithms; Distribution; Fuzzy C-means clustering; gamma distribution; Gamma function; hidden Markov random field (HMRF); Image processing; Image segmentation; Mathematical model; Mathematical models; Mean field theory; Probability distribution functions; Radar imaging; SAR (radar); Shape; Speckle; speckle noise; Statistical analysis; Statistical models; Synthetic aperture radar; synthetic aperture radar (SAR) image segmentation
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3010696

Synthetic aperture radar (SAR) image segmentation is a challenge due to its inherent speckle. Gamma distribution is believed to be an appropriate statistical model to describe the characteristics of speckle in SAR images. In this letter, a fuzzy clustering algorithm based on gamma distribution for SAR image segmentation is proposed, in which the Stirling equation is used to approach the gamma function in the dominator of gamma distribution under the assumption of mean field theory to make the shape parameter <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> derivable. Then, the value range of the estimated <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> is demonstrated to meet the requirement of gamma distribution by Jensen's inequality. Experimental results show that the proposed method gives promising results in SAR image (including large area SAR image) segmentation and effectively suppresses the influence of speckle.