运动、静止目标获取与识别（MSTAR）计划表明，将合成孔径雷达（SAP）图象分割成目标、阴影和背景杂波区域对于从开放环境中进行目标识别是一种有效的手段。但是由于SAP图象所固有的斑点噪声的影响，传统的分割方法很难获得准确的分割。为此提出了一种基于MRF(Markov random field)模型的SAP图象分割算法。用MRF模型描述待分割图象的先验知识，利用最大似然（ML）估计从训练数据中获得图象各区域的先验概率分布，采用Bayes方法，在观测数据基础上，根据分割图象的后验分布所对应的MRF模型的条件概率，利用Metroplis采样器获得最大后验概率（MAP）准则下的图象分割。通过对MSTAR的样本目标图象应用该算法，结果表明它可以获得稳健和准确的分割效果。

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.