Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 51, Issue 1, Pages 217-231Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2012.2201730
Keywords
Classification; hyperspectral imagery; joint sparsity model; kernel methods; sparse representation
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Funding
- National Science Foundation [CCF-1117545, CCF-0728893]
- Army Research Office [58110-MA-II, 60219-MA]
- Office of Naval Research [N102-183-0208]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1117545] Funding Source: National Science Foundation
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In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.
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