
Proceedings Paper
Kernel sparse representation for hyperspectral target detectionFormat | Member Price | Non-Member Price |
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Paper Abstract
A nonlinear kernel-based classifier for hyperspectral target detection is proposed in this paper. The proposed approach
relies on sparsely representing a test sample in terms of all the training samples in a high-dimensional feature space induced
by a kernel function. Specifically, the feature representation of a test pixel is assumed to be compactly expressed as a sparse
linear combination of few atoms from a training dictionary consisting of both background and target training samples in the
same feature space. The sparse representation vector is obtained by decomposing the test pixel over the training dictionary
via a kernelized greedy algorithm, which uses the kernel trick to avoid explicit evaluations of the data in the feature space.
The class label is then determined by comparing the reconstruction accuracy with respect to the background and target
sub-dictionaries using the recovered sparse vector. Designing the classifier in a high-dimensional feature subspace will
implicitly exploit the higher-order structure (correlations) within the data which cannot be captured by a linear model.
Therefore, by projecting the pixels into a kernel feature space and kernelizing the linear sparse representation model, the
data separability between the background and target classes will be shown to be improved, leading to a more accurate
detection performance. The effectiveness of the proposed kernel sparsity model for target detection is demonstrated by
experimental results on real hyperspectral images.
Paper Details
Date Published: 9 May 2012
PDF: 9 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839005 (9 May 2012); doi: 10.1117/12.918722
Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 9 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839005 (9 May 2012); doi: 10.1117/12.918722
Show Author Affiliations
Yi Chen, The Johns Hopkins Univ. (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Trac D. Tran, The Johns Hopkins Univ. (United States)
Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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