Robust Weighting Nearest Regularized Subspace Classifier for PolSAR Imagery

Abstract

Polarimetric synthetic aperture radar (PolSAR) imagery classification is an important part of SAR data interpretation. The number of available labeled samples limits the applications of supervised classifiers. In order to solve this issue, the representation based classification algorithms have been widely used. Usually, PolSAR image features are extracted by various methods, and their divergence is very significant. In the data representation based methods, the feature divergence is ignored in the distance metric, thus the different features have the same metric contributions. In this letter, we propose a robust weighting nearest regularized subspace (NRS) method, which introduces the robust statistics to construct the weights of distance metric according to the feature divergence. This method can increase the representation ability of the training samples by the weighted calculation of the biasing Tikhonov matrix. The experimental results show that the weighted distance metric can boost the original NRS classifier by $\text{1.5}{\%}$ , and prove that the feature divergence should be taken into account in the data representation process.