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Proceedings Paper

Kernel weighted joint collaborative representation for hyperspectral image classification
Author(s): Qian Du; Wei Li
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Paper Abstract

Collaborative representation classifier (CRC) has been applied to hyperspectral image classification, which intends to use all the atoms in a dictionary to represent a testing pixel for label assignment. However, some atoms that are very dissimilar to the testing pixel should not participate in the representation, or their contribution should be very little. The regularized version of CRC imposes strong penalty to prevent dissimilar atoms with having large representation coefficients. To utilize spatial information, the weighted sum of local spatial neighbors is considered as a joint spatial-spectral feature, which is actually for regularized CRC-based classification. This paper proposes its kernel version to further improve classification accuracy, which can be higher than those from the traditional support vector machine with composite kernel and the kernel version of sparse representation classifier.

Paper Details

Date Published: 21 May 2015
PDF: 6 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010V (21 May 2015); doi: 10.1117/12.2179914
Show Author Affiliations
Qian Du, Mississippi State Univ. (United States)
Wei Li, Beijng Univ. of Chemical Technology (China)

Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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