Spectral-spatial classification of hyperspectral image based on discriminant sparsity preserving embedding

The last few years have witnessed the success of sparse representation in hyperspectral image classification. However, the high computational complexity brings some worries to its applications. In this paper, a novel sparse representation based feature extraction algorithm, called discriminant sparsity preserving embedding (DSPE), is proposed by constructing a sparse graph and applying it to the graph-embedding framework. The proposed algorithm encodes supervised information mainly in stage of sparse graph construction, in which only the training samples in the same class are used to calculated the reconstructive coefficients during sparse reconstruction. An approach combining l1-norm and l2-norm is applied to solve the reconstruction weights, where l1-norm ensures the sparsity of the graph weights, l2-norm shrinks the weight coefficients to make the construction more stable and alleviate the reconstruction errors possibly caused by small-size training samples. On the premise of satisfied classification results, here a spectral-spatial classification strategy which takes spatial information into consideration is used to evaluate the efficiency of the proposed algorithm. Experiments on the Indian Pines and Pavia University hyperspectral image datasets demonstrate the superiority of the proposed algorithm.