SAR Image Speckle Filtering With Context Covariance Matrix Formulation and Similarity Test

Abstract

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.