针对合成孔径雷达(SAR) 图像含有大量斑点噪声的特点,基于Contourlet 的多尺度、局部化、方向性和各向

异性等优点,并结合隐马尔科夫树( HMT) 模型和隐马尔科夫场(MRF) ,提出了一种基于Contourlet 域持续性和聚

集性的SAR 图像模糊融合分割算法。该算法有效捕获了Contourlet 子带的持续性和聚集性,并分别用HMT 和

MRF 来刻画,再依据模糊测度,将多尺度HMT 和MRF 有机融合,建立Contourlet 域HMT2MRF 融合模型,并导

出新模型下的最大后验概率(MAP) 分割公式。对实测SAR 图像进行了仿真,仿真结果和分析表明:与小波域上的

HMT2MRF 融合分割及Contourlet 域上HMT 和MRF 分割算法相比,该算法在抑制斑点噪声的同时,有效地提高

了SAR 图像的分割精度-　In view of the speckle noise in the synthetic aperture radar (SAR) images , and based on the Contourlet′s

advantages of multiscale , localization , directionality , and anisot ropy , a new SAR image fusion segmentation

algorithm based on the pe rsis tence and clustering in the Contourlet domain is p roposed. The algorithm captures the

pe rsis tence and clus tering of the Contourlet t ransform , which is modeled by hidden Markov t ree (HMT) and Markov

random field (MRF) , respectively. Then , these two models are fused by fuzzy logic , resulting in a Contourlet

domain HMT2MRF fusion model . Finally , the maximum a poste rior (MAP) segmentation equation for the new fusion

model is deduced. The algorithm is used to emulate the real SAR images . Simulation results and analysis indicate that

the p roposed algorithm effectively reduces the influence of multiplicative speckle noise , imp roves the segmentation

accuracy and p rovides a bet te r visual quality for SAR images ove r the