Speckle Reduction in Polarimetric SAR Imagery with Stochastic Distances and Nonlocal Means

TL;DR

This paper introduces a novel speckle reduction method for PolSAR images using Nonlocal Means and stochastic divergence tests, effectively preserving scattering information and enhancing image quality.

Contribution

It proposes a new filtering technique based on statistical tests with stochastic divergences, extending the Nonlocal Means approach to PolSAR data.

Findings

Outperforms traditional filters like Boxcar, Refined Lee, and IDAN.

Enhances polarimetric entropy and preserves scattering information.

Validated on simulated and real PolSAR data.

Abstract

This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-phi) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.