Doubly Stochastic MRF-based Segmentation of SAR Images

摘要

In this paper, we present an unsupervised texture segmentation algorithm for Synthetic aperture radar (SAR) images based on a multiscale modeling over images in wavelet pyramidal structure. An image consisting of different textures can be considered as a realization of a collection of two interacting random process―the hidden region label process and the observation process. A novel Gaussian Markov random field (GMRF) model is proposed to describe the fill-in of regions at each scale and a multi-level logistic (MLL) MRF model with particular cliques is used to characterize the intrascale and interscale context dependencies. According to sequential maximum a posterior (SMAP) estimate, expectation-maximization (EM) algorithm is adopted to estimate the parameters of GMRF and to label each pixel iteratively from coarse to fine level. The proposed segmentation approach is applied to synthetic image and SAR image and the result shows its performance.