利用结构相似性改进的极化Lee滤波算法

Improved Polarimetric LEE Filter Using the similarity of Structure

由于传统的参数估计方法不够准确,极化Lee滤波在相干斑抑制和细节信息保留不能够很好地同时兼顾。本文基于非局部均值算法的思想,提出了一种改进的极化Lee滤波。首先在SPAN图上计算窗口中像素之间的相似度得到加权权重,计算出中心像素的期望值和极化Lee滤波中的有关参数,同时利用权重对极化协方差矩阵进行非局部均值形式的加权平均,最后根据加权均值和滤波参数得到极化协方差矩阵的Lee滤波结果。单视和多视数据实验结果表明,该算法提高了极化Lee滤波中参数的估计精度,在保留边缘细节的同时不仅有效地抑制了相干斑噪声,对极化信息也有较好的保持能力,为图像的后续工作提供了较为理想的预处理结果。

Reducing the speckle noise and keeping the detail are conflicted because of the parameters in the polarimetric Lee filter. This paper analyzes nonlocal means filter method, presents an optimization polarimetric Lee filter based on Bayesian nonlocal means. This algorithm calculates the similarity coefficient of the elements in SPAN first, then the weights are carried out to get expectation and the parameter in Lee filter, averages the covariance matrices with the weights according to the similarity between the elements, finally all terms of the covariance matrix will be filtered by Lee filter. Compared with traditional algorithm, the experimental results based on single-look and multi-look data show that the algorithm predigests the complexity of nonlocal means, keeps the polarimetric information well, and it also has the advantages of effectively reducing the speckle noise and keeping the detail.