Texture Feature-Based Segmentation of SAR Images Using a Multiresolution Pairwise Markov-GAR Model
Mingtao Ding · Journal of Astronautics · 2007
This paper presents a new method for segmentation of synthetic aperture radar(SAR) images.We take into account 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 pariwise Markov framework can be proposed based on texture feature images witch come from gray level co-occurrence probability statistics.For texture segmentation of SAR images,using the multiresolution maximization of the posterior marginals(MPM) estimate with the corresponding unsupervised segmentation algorithm on those texture feature images.Compared with multiresolution pariwise Markov-GAR model texture segmentation based on gray level images,for some SAR images,the result of experimentation showed that the method used in this paper has a better performance on segmentation precision.