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Erschienen in:
13.12.2014 | Focus
Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery
Erschienen in: Soft Computing | Ausgabe 12/2016
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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.