SAR Target Segmentation Based on Markov Random Field
基于马尔可夫随机场的SAR图象目标分割

Moving and stationary target acquisition and recognition(MSTAR) program has shown that segment synthetic aperture radar(SAR)imagery into taeget,shadow and background clutter regions is a efficient measure in the process of recognition targets in open terrian.But traditional image segmentation methods are unable to achieve precise segmentation owing to the image affected by speckle noise.In this paper, SAR imagery segmentation algorithm based on MRF(Markov random field) is proposed. The prior information about the segmentation image with MRF model is presented, the prior probability distribution of every region is got from training data by maximum likelihood(ML) estimation,the Bayes formulation is adopted to obtain the conditional distribution of the posterior distribution of the segmentation image conditioned on observed image,based on the maximum a posterior(MAP)criterion,the segmentation is abtained by Metroplis algorithm.By applying this algorithm to the MSTAR sample target images,the result demonstrates the algorithm can achieve robust and precise segmentation result.