Abstract
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 <i>a priori</i>, 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.
Highlights
In interpreting and understanding field of Synthetic Aperture Radar (SAR) image, image segmentation plays a very important role(Ma, 2011)
2010 proposed an image segmentation algorithm based on Voronoi tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy
2015 adopts regular tessellation and RJMCMC to segment high resolution SAR images with unknown number of classes, it can determine the right number of classes automatically, but because it adopt regular tessellation technique, the edges of different regions are always ambiguous, after segmentation, the post-processing must be done to obtain the accurate boundaries
Summary
In interpreting and understanding field of Synthetic Aperture Radar (SAR) image, image segmentation plays a very important role(Ma, 2011). Most of image segmentation algorithms mainly segment the homogeneous regions based on a giving number of classes a priori (Ayed,2005; Touzi,1988; Won,1992; Jain,1992). In order to determine the number of classes automatically and segment homogeneous regions accurately simultaneously, the paper presents a segmentation algorithm combining Voronoi tessellation and RJMCMC strategy. The moves in the designed RJMCMC scheme include splitting or merging real classes, updating parameter vector, updating label field, moving position of generating points, birth or death of generating points and birth or death of an empty class. These moves overcome the instability problem of segmentation optimization effectively and determine the number of classes precisely
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