中文摘要：

针对大多数分类方法未能同时考虑图像与特征、类别与特征、类别与类别之间关系的问题,提出了一种基于潜在语义分析（pLSA）和拓扑马尔可夫随机场（Topo-MRF）模型的合成孔径雷达（synthetic aperture radar,SAR）图像的分类算法。实验结果证明了该算法的有效性。

英文摘要：

A pLSA based Topo-Markov random field（MRF） model method for Synthetic Aperture Radar（SAR） image classification is proposed in this paper.A Topo-category learning method is proposed here to represent the relationships by calculating the proportions of points on boundaries to points belong to each class.It has superiorities over consistent quadratic terms as Potts models and complicated quadratic terms.Meanwhile,Local Binary Pattern as well as other typical features is used as the candidates of the input with feature selection strategy.The experiments are carried on the MSRC and SAR image datasets and the results reveal the proposed algorithm＇s efficient performances and superiorities.