Contextual classification of polarimetric sar data through a complex-valued kernel and global energy minimization

This paper addresses the challenges of supervised semantic segmentation using Polarimetric Synthetic Aperture Radar (PolSAR) data for land cover mapping. We extend previous approaches relying on spatial-contextual classifier based on Support Vector Machines (SVMs) and Markov Random Field (MRF) models. The kernel used in this work extends a previously presented complex formulation based on reproducing kernel Hilbert spaces (RKHS). In this paper, we present a symmetrized form of this complex kernel, integrating it with global energy minimization techniques, and show that it provides more accurate predictions. The proposed approach achieves competitive accuracy on benchmark datasets, comparable to those of deep learning algorithms. The method's advantage lies in its lower resource requirements, making it a promising alternative for PolSAR semantic segmentation.