A Texture Feature Fusion-Based Segmentation Method of SAR Images

Presents a new method for segmentation of synthetic aperture radar (SAR) images. A Gaussian autoregressive (GAR) model under a multiresolution pairwise Markov framework can be proposed based on texture feature fusion images from in part 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 fusion images. This method not only use of pixel gray level information, but also the use of pixel space location information, reducing the speckle noise effect for the segmentation. 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 segmentation precision can be improved by the method in this paper.