Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery

Abstract

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or $\mathrm{S}^{3}\mathrm{RC}$, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of $\mathrm{S}^{3}\mathrm{RC}$, named $\mathrm{FS}^{3}\mathrm{RC}$. Experimental results have shown that both the proposed SRC-based approaches, $\mathrm{S}^{3}\mathrm{RC}$ and $\mathrm{FS}^{3}\mathrm{RC}$, could achieve better performance than the other state-of-the-art methods.