Improved De-Orientation Processing for Polarimetric SAR Data Using a Phenomenological Approach

Polarimetric synthetic aperture radar data can be used to retrieve structural and textural information of the surface and is widely used in land cover classification. To extract relevant parameters, the data must first be compensated for the orientation of the polarimetric scattering targets. However, existing methods for de-orientation processing are not able achieve this robustly and the data still suffer from orientation-induced scattering mechanism ambiguity. Here, we present a new approach to de-orientation that greatly improves on existing methods. Our algorithm innovatively employs phenomenological target decomposition theory and the concept of polarization nulls to extract and de-orient orientation-perturbed components of the target. The objective is to manipulate coherency matrix elements of a distributed target to obtain more descriptive target parameters, which are of critical importance in its de-orientation. We applied our approach to C-, L-, and P-band datasets containing built-up areas with different orientations. In contrast to existing methods, our de-orientation algorithm led to targets of all orientations being identified as having similar scattering characteristics. We also demonstrated that, an improved polarimetric target decomposition performance is achieved when the proposed de-orientation processing is incorporated into model-based decompositions.