An Unsupervised Scattering Mechanism Classification Method for PolSAR Images

Abstract

This letter concentrates on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the combinations of dominant and secondary scattering mechanisms. With three metrics extracted from the observed coherency matrix, an unsupervised classifier is proposed to classify PolSAR pixels into eight combinations of surface scattering, double-bounce scattering, and volume scattering. When applying the proposed method to simulated data, the Kappa coefficient is 0.891. It effectively classifies the dominant mechanism, and the Kappa coefficient is 0.127 higher than that of the H/ $\alpha$ method. Experiment using uninhabited aerial vehicle SAR data shows that the proposed method is able to identify secondary mechanism in forests and urban areas. This method is not only a good classifier free of specific polarimetric decomposition but also can serve as a preclassification step of sophisticated classification scheme.