Speckle filtering of synthetic aperture radar (SAR) image is a necessary pre-processing for many subsequent applications. The challenge lies in how to adaptively select a sufficient number of similar pixels for an unbiased estimator generation. A novel SAR speckle filter is proposed and the core idea contains two aspects. Firstly, a context covariance matrix representation is developed within a local neighborhood to characterize the contexture information. Then, the Wishart statistic test is extended to examine the similarity of context covariance matrices. The extended similarity test indicator derived from context covariance matrices is verified to be sensitive for similar pixel localization. Thereafter, a sample averaging estimator is adopted based on the similar samples determined by the context covariance matrices similarity test (the proposed method is named as the CCM+SimiTest). Furthermore, a fast similarity test computation scheme is established which can handle large images smoothly even with a normal laptop. Intensive experimental studies with Radarsat-2, MiniSAR and ALOS-2 datasets are carried out. Comparisons with several state-of-the-art methods from both subjective and objective viewpoints demonstrate the superiority of the proposed method.

中文翻译：

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利用上下文协方差矩阵公式和相似性测试进行 SAR 图像散斑滤波


合成孔径雷达（SAR）图像的散斑滤波是许多后续应用的必要预处理。挑战在于如何自适应地选择足够数量的相似像素来生成无偏估计器。提出了一种新型SAR散斑滤波器，其核心思想包含两个方面。首先，在局部邻域内开发上下文协方差矩阵表示来表征上下文信息。然后，扩展 Wishart 统计检验来检查上下文协方差矩阵的相似性。验证从上下文协方差矩阵导出的扩展相似性测试指标对于相似像素定位是敏感的。此后，基于上下文协方差矩阵相似性测试确定的相似样本，采用样本平均估计器（所提出的方法被命名为CCM+SimiTest）。此外，建立了一种快速相似性测试计算方案，即使使用普通笔记本电脑也可以顺利处理大图像。利用 Radarsat-2、MiniSAR 和 ALOS-2 数据集进行了深入的实验研究。从主观和客观角度与几种最先进的方法进行比较证明了所提出方法的优越性。