A Model-free Four Component Scattering Power Decomposition for
Polarimetric SAR Data
Abstract
Target decomposition methods of polarimetric Synthetic Aperture Radar
(PolSAR) data explain scattering information from a target. In this
regard, several conventional model based methods utilize scattering
power components to analyze polarimetric SAR data. However, the typical
hierarchical process to enumerate power components uses various
branching conditions, leading to several limitations. These techniques
assume ad hoc scattering models within a radar resolution cell.
Therefore, the use of several models makes the computation of scattering
powers ambiguous. Some common issues of model-based decompositions are
related to the compensation of the orientation angle about the radar
line of sight and the negative power components’ occurrence. We propose
a model-free four-component scattering power decomposition that
alleviates these issues. In the proposed approach, we use the
non-conventional 3D Barakat degree of polarization to obtain the
scattered electromagnetic wave’s polarization state. The degree of
polarization is used to obtain the even-bounce, odd-bounce, and diffused
scattering power components. Along with this, a measure of target
scattering asymmetry is also proposed, which is then suitably utilized
to obtain the helicity power. All the power components are
roll-invariant, nonnegative and unambiguous. In addition to this, we
propose an unsupervised clustering technique that preserves the
dominance of the scattering power components for different targets. This
clustering technique assists in understanding the importance of diverse
scattering mechanisms based on target characteristics. The technique
adequately captures the clusters’ variations from one target to another
according to their physical and geometrical properties. This study
utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR
data. These two data sets are used to show the decomposition powers’
effectiveness and the apparent interpretability of the clustering
results.