Abstract
The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. The sparse subspace clustering (SSC) algorithm is one of the popular used clustering algorithm for high dimensionality data. But, SSC has not considered the spectral and spatial information fully, so it is not satisfied for Hyperspectral Imagrery (HSI) clustering. In this paper, a novel similarity matrix construction methods are proposed which combined the high spectral correlation and rich spatial connection. Firstly, we utilize the cosine similarity of sparse representation vector to construct a novel similarity matrix. Then, the similarity matrix based on Euclidean distance of the sparse representation vector can connect spectral correlation with spatial information. Several experiments on HSIs demonstrated that the proposed algorithms are effective for hyperspectral images (HSIs) clustering.
Q. Yan et al.—These authors are contributed equally to the paper as first authors.
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Acknowledgments
This work was supported by the National Science Foundation for China (No.61602002), AnHui University Youth Skeleton Teacher Project (E12333010289), Anhui University Doctoral Scientific Research Start-up Funding (J10113190084), China Postdoctoral Science Foundation (2015M582826).
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Yan, Q., Ding, Y., Zhang, JJ., Xun, LN., Zheng, CH. (2017). Similarity Matrix Construction Methods in Sparse Subspace Clustering Algorithm for Hyperspectral Imagery Clustering. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_61
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DOI: https://doi.org/10.1007/978-3-319-63309-1_61
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