Hyperspectral Image Classification With Robust Sparse Representation

Recently, the sparse representation-based classification (SRC) methods have been successfully used for the classification of hyperspectral imagery, which relies on the underlying assumption that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples among the whole training dictionary. However, the SRC-based methods ignore the sparse representation residuals (i.e., outliers), which may make the SRC not robust for outliers in practice. To overcome this problem, we propose a robust SRC (RSRC) method which can handle outliers. Moreover, we extend the RSRC to the joint robust sparsity model named JRSRC, where pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few training samples and outliers. The JRSRC can also deal with outliers in hyperspectral classification. Experiments on real hyperspectral images demonstrate that the proposed RSC and JRSRC have better performances than the orthogonal matching pursuit (OMP) and simultaneous OMP, respectively. Moreover, the JRSRC outperforms some other popular classifiers.