A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameters Based Unsupervised Classification Scheme Using a Geodesic Distance

TL;DR

This paper introduces a novel framework for PolSAR data analysis that directly derives scattering power components and roll-invariant parameters using geodesic distances, enhancing unsupervised classification accuracy.

Contribution

The paper presents a generic scattering power factorization framework that directly computes scattering components and roll-invariant parameters using geodesic distances, differing from traditional hierarchical models.

Findings

Effective scattering power components obtained

Improved unsupervised classification results

Validated on RADARSAT-2 and ALOS-2 datasets

Abstract

We propose a generic Scattering Power Factorization Framework (SPFF) for Polarimetric Synthetic Aperture Radar (PolSAR) data to directly obtain $N$ scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized random volume model. The similarity measure is derived using a geodesic distance between pairs of $4\times4$ real Kennaugh matrices. In standard model-based decomposition schemes, the $3\times3$ Hermitian positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the non-negative scattering power components. Furthermore, the framework along the geodesic distance is effectively used to obtain specific roll-invariant parameters which are then utilized to design an unsupervised classification scheme. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.