Optimal SVM Classification for Compact Polarimetric Data Using Stokes Parameters

Abstract

In this paper, our objective is twofold: first, to assess the potential of the new compact polarimetry imaging radar system called hybrid-polarimetry (CL-pol): circular transmitted polarization and coherent dual linear receive polarizations for full characterization and exploitation of the backscattered field. Useful characteristics that are unique to the hybrid-polarity architecture are invariance to geometrical orientations and minimizing on-board resource requirements. Second, to develop a classification polarimetric method based on the support vector machine (SVM) which uses full- and the compact-pol modes. We present a study of the polarimetric information content derived from the decomposition for the CL-mode using Stokes parameter data products and from Freeman-Durden-decomposition derived from the full-pol imaging mode. We compare SVM classification both among the partial polarimetric datasets and against the full quad-pol dataset. We illustrate our results by using the polarimetric SAR images of Algiers city in Algeria acquired by the RadarSAT2 (FQ19) in C-band.