贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较好地保持了边缘细节和点目标。通过分析合成孔径雷达(SAR)图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出一种根据像素间相似程度自适应选取滤波参数的方法。实验结果验证了本文算法的有效性。

The Bayesian non-local means filter is adapted to the polarimetric synthetic aperture radar (SAR) multiplicative speckle noise, which effectively reduces the speckle noise while keeping the details. Through analyzing the speckle statistics of multi-look SAR images combined with Bayesian non-local means filter, we conclusde that the similarity function in the multi-look SAR image is according to that of the single-look SAR image, which is proved in this paper. Then, a new amended similarity function is proposed. Finally, an adaptive filtering parameter selection method is proposed based on similarity between pels to solve the problem that a fixed parameter could influence the filter performance. The proposed method is validated on real polarimetric SAR images through comparisons with the original algorithm.