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A Target-to-Mechanism Mapping Network for Polsar Data Interpretation

Model-based decompositions are powerful tools for scattering mechanism interpretation of polarimetric synthetic aperture radar (PolSAR) data. By incorporating their refined physical scattering models and utilizing the excellent nonlinear data fitting capability of neural networks, a target-to-mechanism mapping network is proposed. Inputting the polarimetric features defined in the normalized polarimetric feature space, the proposed network outputs the normalized powers of four scattering components. Experimental studies demonstrate that the trained network on Pi-SAR X-band PolSAR data shows good interpretation performance and generality on the cross-observation perspective Pi-SAR X-band PolSAR data and the cross-frequency Radardat-2 C-band PolSAR data. In addition, the proposed approach has a fast interpretation speed.