Unsupervised classification of scattering behaviour using hybrid-polarimetry

Abstract

This study presents an unsupervised algorithm for classification of scattering behaviour using hybrid-polarimetric (hybrid-Pol) data. The authors present a maximum likelihood estimation-based unsupervised land cover classification algorithms for hybrid-PolSAR image. This classification technique follows from the m - δ decomposition of hybrid-Pol images. Introduction of a statistical treatment is the major contribution of the current algorithm. Performance of the hybrid- Pol algorithms have been assessed with respect to Freeman-Durden decomposition of fully polarimetric SAR data. The authors have demonstrated, using two different datasets, that proposed algorithm not only gives better overall classification performance, it is also able to classify all the three major types of scattering mechanisms, whereas the existing hybrid- PolSAR classification algorithms mostly fail to classify one of the scattering types. © The Institution of Engineering and Technology 2013.