Shen Yinbin, Ma Xiaoshuang, Zhu Shengyuan, Xu Jiangong
China JIKAN Research Institute of Engineering Investigations and Design, Co., Ltd., Xi'an 710000, China.
School of Resources and Environmental Engineering/Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China.
Sensors (Basel). 2021 Nov 6;21(21):7393. doi: 10.3390/s21217393.
Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism.
在大多数情况下，去斑是使用极化合成孔径雷达（PolSAR）数据的应用中的关键预处理步骤。本文提出了一种基于非局部加权线性最小均方误差（NWLMMSE）滤波器的技术，用于极化合成孔径雷达（PolSAR）斑点滤波。在用线性最小均方误差（LMMSE）估计器对一个像素进行滤波的过程中，基于样本像素邻域与待处理像素之间的统计相等性，采用非局部均值的思想来评估估计器中样本的权重。然后推导出了NWLMMSE估计器。在预处理中，采取了一个有效的步骤对像素进行预分类，目的是保留点目标，并考虑后续滤波器中像素间散射机制的相似性。使用一幅模拟图像和两幅真实世界的PolSAR图像进行说明，实验表明该滤波器在减少斑点方面是有效的，同时能有效地保留强点目标、边缘以及极化散射机制。
