Abstract
ABSTRACTThis letter presents an SAR intensity image segmentation algorithm by combining Voronoi tessellation and Gamma Mixture Model (GaMM). By Voronoi tessellation, the domain of the image is partitioned into a collection of sub-regions, which acts on region based segmentation to overcome speckle noise. GaMMs defined on the sub-regions can be used to model the complicate distributions of the homogeneous regions in the image. Following Bayesian theory, the posterior distribution as a segmentation model can be formulated. Finally, Markov Chain Monte Carlo (MCMC) algorithm is designed to obtain the optimal segmentation by simulating the segmentation model. The experiments carried out by the proposed and comparing algorithms fully demonstrate the effectiveness of the proposed algorithm.
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