基于改进FCM与MRF的SAR图像分割

摘要

合成孔径雷达(synthetic aperture radar ,SAR)图像相干斑点噪声强,缺乏背景与目标先验知识,导致分割困难.针对以上问题,提出了基于改进模糊C均值聚类(fuzzy C‐means clustering ,FCM )与马尔可夫随机场(M arkov random field ,M RF)的分割算法.首先,利用自适应非局部均值滤波和基于直方图峰值点的初始聚类中心选定规则,提升快速FCM 算法效率;然后分别用改进 FCM 算法与MRF对SAR图像进行分割,并通过构建联合隶属度矩阵自适应选择最优分割区域;最后利用形态学操作对结果进行优化.实验表明,所提算法具有较好的抗噪性能,能够快速有效地分割多类SAR图像.%The problems of strong coherent speckle noise and the lack of prior know ledge of background and targets ,make segmentation difficult in synthetic aperture radar (SA R ) images .A segmentation algorithm based on improved fuzzy C‐means (FCM ) clustering and M arkov random field (M RF) is proposed .Firstly ,the effi‐ciency of fast FCM is improved by the adaptive non‐local mean filter and the initial clustering center selection rule based on histogram peak points .Secondly ,the SAR image is segmented by improved FCM and M RF re‐spectively ,and the optimal segmentation regions are adaptively selected by constructing the joint membership matrix .Finally ,the final segmentation result is optimized by morphological operations .The experimental re‐sults show that the proposed algorithm has better anti‐noise performance and can segment multi‐class SAR ima‐ges quickly and efficiently .