Hyperspectral image classification using sparse representation-based classifier

Based on the idea of SRC(Sparse Representation based Classification), a novel approach HSIC-SRC is proposed in this paper. Unlike most existing algorithms for HSIC(HSI Classification) via sparse representation, our main contributions lie in two aspects, 1) Considering the performance of SRC depending on the quality of dictionary, we employ LC-KSVD(Label Consistent KSVD) algorithm which joints the regularization of discriminative sparse-code error and the regularization of classification error into the sparsity model, to learn a more `compact´ and `discriminative´ dictionary for sparse representation, hence we can achieve better accuracy on classification; 2) Considering the neighboring pixels usually composed by similar materials, their spectral characteristics are highly correlated. Therefore, we employ the contextual information(or spatial information) into the sparse model, to obtain more optimized sparse representation of pixels. The proposed HSIC-SRC is applied to the well known hyperspectral image `University of Pavia´ for classification, and experimental results show that it outperforms some other state-of-the-art classification algorithms, such as SVM, SP, OMP and SPG-l₁, with only one simple linear classifier.