Performance evaluation and segmentation for synthetic aperture radar image using mixture multiscale autoregressive model and bootstrap technique

An unsupervised segmentation and its performance evaluation technique are proposed for synthetic aperture radar (SAR) image based on the mixture multiscale autoregressive (MMAR) model and the bootstrap method. The segmentation-evaluation techniques consist of detecting the number of image regains, estimating MMAR parameters by using bootstrap stochastic annealing expectation-maximization (BSAEM) algorithm, and classifying pixels into region by using Bayesian classifier. Experimental results demonstrate that the evaluation operation is robust, and the proposed segmentation method is superior to the traditional single resolution techniques, and considerably reduces the computing time over the EM algorithm.