Man-Made Target Structure Recognition With Polarimetric Correlation Pattern and Roll-Invariant Feature Coding

Man-made target recognition is of great significance for many applications within microwave remote sensing. The scattering diversity of various man-made target structures makes radar target identification a difficult task. This work aims at mitigating this issue by mining and utilization of man-made target scattering diversity in polarimetric rotation domain with the interpretation tool of polarimetric correlation pattern. The optimal polarimetric roll-invariant feature set is collected from polarimetric correlation pattern. Then, a polarimetric roll-invariant feature coding scheme is developed for man-made target structure recognition. Moreover, polarimetric radar measurement errors in terms of channel coupling and imbalance are also considered. Experimental studies with electromagnetic computation datasets including canonical structures and an unmanned aerial vehicle (UAV) target and real spaceborne polarimetric synthetic aperture radar (PolSAR) data of a ship target are carried out. Compared with the Cameron decomposition, the proposed method exhibits better recognition performance and stronger robustness, especially for oriented man-made structures.