Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

This paper presents an algorithm dealing with initial segmentation of speckled Synthetic Aperture Radar (SAR) intensity images in order to automatically determine the number of homogeneous regions. Taking this problem into account, segmentation procedure utilizing splitting and merging is designed, iteratively. The proposed approach is based upon Bayesian inference, a maximum likelihood gamma distribution parameter estimator, and a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using of image splitting operation, SAR image is partitioned into finite regions iteratively, until all individual regions are coherent. Then each region is assigned a unique label to indicate the class to which the homogeneous region belongs. The intensities of pixels in each coherent region are assumed to satisfy identical and independent gamma distribution. Then an RJMCMC scheme is designed to simulate the posterior distribution in order to estimate the number of components and delineate an initial segmentation. Thus, the main purpose of this research is to define the number of homogeneous regions rather than a perfect segmentation, i.e. model outputs can be served for unsupervised segmentation methodologies as prior information. The results obtained from Radarsat-1/2 of SAR intensity images show that the proposed algorithm is both capable and reliable in defining the accurate number of homogeneous regions in a wide variety of SAR intensity images, comprising a high level of speckle noise.