A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameters Based Unsupervised Classification Scheme Using a Geodesic Distance
This work addresses the need for improved model-based decomposition and classification in remote sensing for PolSAR data analysis, though it appears incremental by building on existing decomposition schemes.
The authors tackled the problem of decomposing Polarimetric Synthetic Aperture Radar (PolSAR) data into scattering power components by proposing a Scattering Power Factorization Framework (SPFF) that uses a geodesic distance for similarity measures and roll-invariant parameters, resulting in an unsupervised classification scheme tested on RADARSAT-2 and ALOS-2 images of San Francisco.
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.