Segmentation of high-resolution SAR image with unknown number of classes based on regular tessellation and RJMCMC algorithm

This article presents a statistics- and region-based approach to segmentation of synthetic aperture radar (SAR) images. The proposed approach can automatically determine the number of classes and segment the image simultaneously. First of all, an image domain is partitioned into a set of blocks by regular tessellation and the image is modelled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent gamma distribution. The Bayesian paradigm is followed to build an image segmentation model. Then, a Reversible Jump Markov Chain Monte Carlo scheme is designed to simulate the segmentation model, which determines the number of classes and segments the image roughly. Furthermore, in order to improve the accuracy of the segmentation results, refined operation is performed. The results obtained from both real and simulated SAR images show that the proposed approach works well and efficient.

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