Hyperspectral image classification via kernel fully constrained least squares

Abstract

This paper presents a new spatial-spectral classification method for hyperspectral images, which consists of three main techniques. Firstly, fully constrained least squares (FCLS) that is common in hyperspectral unmixing is investigated for hyperspectral image classification in kernel Hilbert space. Secondly, the spatial-spectral information of hyperspectral images is exploited to improve the classification performance of kernel-based FCLS (KFCLS) by taking advantage of a weighted H1 norm-based regularization term. Finally, the spatial and label information of training pixels is furthermore incorporated into KFCLS to deal with the scenarios dominated by limited training pixels. Experimental results on two real hyperspectral images demonstrate the effectiveness of the proposed method.