Polarimetric SAR Image Smoothing with Stochastic Distances

TL;DR

This paper introduces a novel smoothing method for polarimetric SAR images using stochastic distances, effectively reducing noise while preserving details across various visualization spaces.

Contribution

The paper proposes a new smoothing technique based on stochastic distances for PolSAR images, improving noise reduction and detail preservation.

Findings

Effective noise reduction in PolSAR images

Preservation of fine details across visualization spaces

Enhanced image visualization quality

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. They also suffer from speckle noise which reduces the signal-to-noise ratio. Smoothing techniques have been proposed in the literature aiming at preserving different features and, analogously, projections from the cone of Hermitian positive matrices to different color representation spaces are used for enhancing certain characteristics. In this work we propose the use of stochastic distances between models that describe this type of data in a Nagao-Matsuyama-type of smoothing technique. The resulting images are shown to present good visualization properties (noise reduction with preservation of fine details) in all the considered visualization spaces.