Texture segmentation of SAR image using multiresolution pairwise Markov model

A new method for segmentation of synthetic aperture radar (SAR) images is presented to consider spatial distributed characters between pixels of SAR images as well as the local means and variances statistics of gray level, a Gaussian autoregressive (GAR) model under a multiresolution pairwise Markov framework. Based on texture feature images from gray level co-occurrence probability statistics, we examine the texture segmentation of SAR image suing the multi-resolution maximization of the posterior marginal (MPM) estimate with corresponding unsupervised segmentation algorithm on those texture feature images. For some SAR images, compared with multiresolution pariwise Markov-GAR model texture segmentation based on gray level images, the results of experimentation show that the proposed method in this paper has a better performance on segmentation precision.