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. Meanwhile, spatial information, that 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 spatial-neighborhood-integrated SRC method, abbreviated as SN-SRC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Experimental results have shown that the proposed SN-SRC approach could achieve better performance than the other state-of-the-art methods, especially with limited training samples.
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Jia, S., Xie, Y., Zhu, Z. (2015). Integration of Spatial and Spectral Information by Means of Sparse Representation-Based Classification for Hyperspectral Imagery. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_10
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DOI: https://doi.org/10.1007/978-3-319-13356-0_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13355-3
Online ISBN: 978-3-319-13356-0
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