Spatial-Spectral Graph Regularized Kernel Sparse Representation for Hyperspectral Image Classification

Abstract

This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two spatial-spectral constraint terms are appended to the sparse recovery model of kernel sparse representation. The first one is a graph-based spatially-smooth constraint which is utilized to describe the contextual information of hyperspectral images. The second one is a spatial location constraint, which is exploited to incorporate the prior knowledge of the location information of training pixels. Then, an efficient alternating direction method of multipliers is developed to solve the corresponding minimization problem. At last, the recovered sparse coefficient vectors are used to determine the labels of test pixels. Experimental results carried out on three real hyperspectral images point out the effectiveness of the proposed method.