Polarimetric SAR Classification Using Ridge Regression-Based Polarimetric-Spatial Feature Extraction

Abstract

A polarimetric synthetic aperture radar (PolSAR) image classification is introduced in this work. The proposed method called as ridge regression-based polarimetric-spatial (RRPS) feature extraction generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The proposed RRPS method with a closed form solution has high performance in PolSAR image classification using small training sets.