Polarimetric Coherence Pattern: A Visualization and Characterization Tool for PolSAR Data Investigation

Abstract

Polarimetric coherence, which has the potential to reveal physical properties of scatterers, is an important source for polarimetric synthetic aperture radar (PolSAR) data investigation. Target structure and orientation relative to the PolSAR illumination direction are key factors affecting the polarimetric coherence degree. The relative orientation between a sensor and a target can be adjusted using the rotating processing along the radar’s line of sight. The main idea of this paper is to extend the traditional polarimetric coherence at a given rotation state ( $\theta =0)$ to the rotation domain ( $\theta \in [ {-\pi ,\pi } ))$ along the radar’s line of sight for hidden information exploration. A visualization and characterization tool named as a polarimetric coherence pattern for two arbitrary polarization channels is proposed and developed. This interpretation tool is able to view the variation of polarimetric coherence in the rotation domain containing rich orientation diversity information which is seldom considered. A set of characterization features are derived to completely describe a polarimetric coherence pattern thereafter. Experimental studies with unmanned aerial vehicle SAR (UAVSAR) PolSAR data over crop areas have validated that polarimetric coherence patterns vary in terms of polarization combinations and crop types. The proposed characterization features show good potential to differentiate polarimetric responses from different land covers. Furthermore, a classification scheme combining the selected proposed features and the commonly used roll-invariant features is developed for quantitative and application investigation. Comparison studies with both UAVSAR and Airborne SAR (AIRSAR) data clearly demonstrate the superiority of the proposed classification to the conventional classification with only roll-invariant features. The overall classification accuracies for the seven and eleven land covers of UAVSAR and AIRSAR data are, respectively, increased from 90.21% and 93.87% to 95.12% and 94.63% by the proposed classification scheme. This paper also demonstrates the importance of and potential for utilizing the complementary advantages of roll-invariant features and the proposed roll-variant features.