Locality constrained low-rank representation for hyperspectral image classification

This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be guaranteed. In this paper, to sidestep this limitation, we simultaneously exploit the spectral similarity and spatial information of pixels to design a local constraint as the regularizer of LRR, which is referred to as the locality constrained LRR (LCLRR). The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of our proposed method.