Kernel low rank representation for hyperspectral image classification

In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the linearly non-separable problems directly, we transformed the linear LRR classifier to a non-linear