Feature-Based Nonlocal Polarimetric SAR Filtering

Xiaoli Xing,Shuai Yang,Xiuguo Liu,Qihao Chen

doi:10.3390/rs9101043

Xiaoli Xing, Shuai Yang + Show 2 more

Open Access

https://doi.org/10.3390/rs9101043

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Journal: Remote sensing	Publication Date: Oct 13, 2017
Citations: 12	License type: CC BY 4.0

Affiliation: China University of Geosciences

Abstract

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV ) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

Highlights

Synthetic aperture radar (SAR) explores the characterization of the Earth’s surface in the microwave range, which makes it possible to observe and retrieve data under all weather conditions and all times
The proposed filtering method consists of three main parts: (1) the representation of the Polarimetric SAR (PolSAR) data characteristics and Pauli decomposition; (2) the heterogeneity coefficient of variance (CV) based on the polarimetric whitening filtering (PWF); (3) the nonlocal mean (NLM) filtering based on the CV and the Pauli basis
The features of the heterogeneity and polarimetric scattering are introduced to the proposed filtering method for PolSAR images

Summary

Introduction

Synthetic aperture radar (SAR) explores the characterization of the Earth’s surface in the microwave range, which makes it possible to observe and retrieve data under all weather conditions and all times. The adaptive filtering methods are developing rapidly by using weighted averages of homogeneous pixels with similar scattering property. These filter algorithms have been widely applied to reduce speckle by using a weighted combination of the homogeneous sample and its mean. The bilateral filter calculates the weighted average based on the similarity between pixels in the local windows of the spatial and radiometric domains [7,8,9]. These filters are performed by the pixels close to the target, which implies these pixels that are similar in the local window. The notable differences between pixels exist in the heterogeneous regions, and the preservation of the image details is affected

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Results

Conclusion