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

For the problem of that the traditional fuzzy clustering algorithm cannot precisely describe the distribution characteristics of synthetic aperture radar (SAR) intensity image and overcome the inherently existed speckle noises, the SAR image segmentation algorithm of regionalized fuzzy clustering based on the Gamma mixture model (GaMM) with variable shape parameter is proposed. Firstly, the image domain is completely divided into several Voronoi polygons by Voronoi tessellation. Assuming that the pixel intensities follow the GaMM with variable shape parameter, the non-similarity measure 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 by combining the GaMM and regularization term with spatial constraint between neighbor Voronoi polygons. In the parameter estimation procedure, the moving-updating operations are 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-tolerant ability of the proposed algorithm. © 2020, Editorial Office of Control and Decision. All right reserved.