SAR IMAGE SEGMENTATION WITH UNKNOWN NUMBER OF CLASSES COMBINED VORONOI TESSELLATION AND RJMCMC ALGORITHM

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. III-7; pp. 119 - 124
• Main Authors: Zhao, Q. H., Li, Y., Wang, Y.
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 07.06.2016; Copernicus Publications
• Subjects: Bayesian analysis; Chain mobility; Computer simulation; Image segmentation; Markov analysis; Markov chains; Monte Carlo simulation; Normalizing; Radar imaging; Random variables; Synthetic aperture radar; Tessellation; Voronoi graphs
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-III-7-119-2016

This paper presents a novel segmentation method for automatically determining the number of classes in Synthetic Aperture Radar (SAR) images by combining Voronoi tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy. Instead of giving the number of classes a priori, it is considered as a random variable and subject to a Poisson distribution. Based on Voronoi tessellation, the image is divided into homogeneous polygons. By Bayesian paradigm, a posterior distribution which characterizes the segmentation and model parameters conditional on a given SAR image can be obtained up to a normalizing constant; Then, a Revisable Jump Markov Chain Monte Carlo(RJMCMC) algorithm involving six move types is designed to simulate the posterior distribution, the move types including: splitting or merging real classes, updating parameter vector, updating label field, moving positions of generating points, birth or death of generating points and birth or death of an empty class. Experimental results with real and simulated SAR images demonstrate that the proposed method can determine the number of classes automatically and segment homogeneous regions well.