Abstract
In this paper, a new local collaborative representation-based method is proposed for the hyperspectral image classification. First, some significant atoms are selected to represent the neighbors of the pixels based on the collaborative representation algorithm via replacing L1 with L2 to reduce the representation cost. Then, the query pixel is considered as a linear combination of these selected active atoms belong to different classes, and the ultimate classification is carried out based on the contribution of each class to the query pixel and its local neighbors. Experimental results on the real hyperspectral image confirm the effectiveness, accuracy of the method proposed.
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Peng, Y., Yan, Y., Zhu, W., Zhao, J. (2014). Hyperspectral Image Classification Using Local Collaborative Representation. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_22
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DOI: https://doi.org/10.1007/978-3-662-45646-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
Online ISBN: 978-3-662-45646-0
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