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 S3RC\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{S}^{3}\mathrm{RC}$$\end{document}, 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 S3RC\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{S}^{3}\mathrm{RC}$$\end{document}, named FS3RC\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{FS}^{3}\mathrm{RC}$$\end{document}. Experimental results have shown that both the proposed SRC-based approaches, S3RC\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{S}^{3}\mathrm{RC}$$\end{document} and FS3RC\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{FS}^{3}\mathrm{RC}$$\end{document}, could achieve better performance than the other state-of-the-art methods.