Polarimetric SAR Terrain Classification Using Polarimetric Features Derived from Rotation Domain

Abstract

Terrain classification is an important application for understanding and interpreting Polarimetric Synthetic Aperture Radar (PolSAR) images. One common PolSAR terrain classification uses roll-invariant feature parameters such as H/A/a/SPAN. However, the back scattering response of a target is closely related to its orientation and attitude. This frequently introduces ambiguity in the interpretation of scattering mechanisms and limits the accuracy of the PolSAR terrain classification, which only uses roll-invariant feature parameters for classification. To address this problem, the uniform polarimetric matrix rotation theory, which interprets a target’s scattering properties when its polarimetric matrix is rotated along the radar line of sight and derives a series of polarimetric features to describe hidden information of the target in the rotation domain was proposed. Based on this theory, in this study, we apply the polarimetric features in the rotation domain to PolSAR terrain discrimination and classification, and develop a PolSAR terrain classification method using both the polarimetric features in the rotation domain and the roll-invariant features of H/A/a/SPAN. This method also uses both the selected polarimetric feature parameters in the rotation domain and H/A/a/SPAN as input for a Support Vector Machine (SVM) classifier and achieves better classification performance by complementing the terrain discrimination abilities of both. Results from comparison experiments based on AIRSAR and UAVSAR data demonstrate that compared with the conventional method, which only uses H/A/a/SPAN as SVM classifier input, the proposed method can achieve higher classification accuracy and better robustness. For fifteen terrain classes of AIRSAR data, the total classification accuracy of the proposed method was 92.3%, which is higher than the 91.1% of the conventional method. Moreover, for seven terrain classes of multi-temporal UAVSAR data, the averaged total classification accuracy of the proposed method was 95.72%, which is much higher than the 87.80% of the conventional method. These results demonstrate that our proposed method has better robustness for multi-temporal data. The research also demonstrates that mining and extracting polarimetric scattering information of a target deep in the rotation domain provides a feasible new approach for PolSAR image interpretation and application.

Keywords

• Polarimetric Synthetic Aperture Radar (PolSAR)
• Polarimetric features
• Rotation domain
• Support Vector Machine (SVM)
• Terrain classification