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.
Speckle Filtering of GF-3 Polarimetric SAR Data with Joint Restriction Principle