SAR image segmentation algorithm of regionalized fuzzy clustering based on the Gamma mixture model with variable shape parameters

LI Xiao-li1 ZHAO Quan-hua1 LI Yu1

(1.School of Geomatics, Liaoning Technical University, Fuxin, Liaoning Province, China 123000)
【Knowledge Link】speckle noise

【Abstract】With regard to the problem that the traditional fuzzy clustering algorithm cannot precisely describe the distribution characteristics of synthetic aperture radar (SAR) intensity image and overcome the inherent speckle noises, the SAR image segmentation algorithm of regionalized fuzzy clustering based on the Gamma mixture model (GaMM) with variable shape parameters is proposed. First, the image domain is completely divided into several Voronoi polygons by Voronoi tessellation. Assuming that the pixel intensities follow the GaMM with variable shape parameters, the dissimilarity between the intensities of pixels in Voronoi polygons and clusters is described by the negative logarithmic function of the GaMM. Then, the regionalized fuzzy objective function is defined with the combination of the GaMM and regularization term with spatial constraint between neighbor Voronoi polygons. In the parameter estimation procedure, the moving-updating operation is designed to solve the implicit parameters according to the criterion of minimizing the objective function. The qualitative and quantitative analyses for the segmentation results of real and simulated SAR images effectively prove the fitting ability of the regionalized GaMM with variable shape parameters to SAR data and the noise immunity of the proposed algorithm.

【Keywords】 variable shape parameter; Gamma mixture model; Voronoi tessellation; spatial constraint; fuzzy clustering; SAR image segmentation;

【DOI】

【Funds】 National Natural Science Foundation of China (41271435, 41301479) Natural Science Foundation of Liaoning Province, China (2015020090) Postgraduate Education Innovation Program of Liaoning Technical University, China (YB201605)

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This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 07, Pages 1639-1644

July 2020

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Abstract

  • 0 Introduction
  • 1 Algorithm description
  • 2 Experiments and discussion
  • 3 Conclusions
  • References