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Remote Sens. 2016, 8(12), 985;

Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations

School of Computer Science, Wuhan University of Science and Technology, Wuhan 430065, China
Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
Center for Research in Computer Vision, University of Central Florida, Orlando, FL 32816, USA
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao and Prasad S. Thenkabail
Received: 11 August 2016 / Revised: 13 November 2016 / Accepted: 17 November 2016 / Published: 30 November 2016
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Sparse representation (SR)-driven classifiers have been widely adopted for hyperspectral image (HSI) classification, and many algorithms have been presented recently. However, most of the existing methods exploit the single layer hard assignment based on class-wise reconstruction errors on the subspace assumption; moreover, the single-layer SR is biased and less stable due to the high coherence of the training samples. In this paper, motivated by category sparsity, a novel multi-layer spatial-spectral sparse representation (mlSR) framework for HSI classification is proposed. The mlSR assignment framework effectively classifies the test samples based on the adaptive dictionary assembling in a multi-layer manner and intrinsic class-dependent distribution. In the proposed framework, three algorithms, multi-layer SR classification (mlSRC), multi-layer collaborative representation classification (mlCRC) and multi-layer elastic net representation-based classification (mlENRC) for HSI, are developed. All three algorithms can achieve a better SR for the test samples, which benefits HSI classification. Experiments are conducted on three real HSI image datasets. Compared with several state-of-the-art approaches, the increases of overall accuracy (OA), kappa and average accuracy (AA) on the Indian Pines image range from 3.02% to 17.13%, 0.034 to 0.178 and 1.51% to 11.56%, respectively. The improvements in OA, kappa and AA for the University of Pavia are from 1.4% to 21.93%, 0.016 to 0.251 and 0.12% to 22.49%, respectively. Furthermore, the OA, kappa and AA for the Salinas image can be improved from 2.35% to 6.91%, 0.026 to 0.074 and 0.88% to 5.19%, respectively. This demonstrates that the proposed mlSR framework can achieve comparable or better performance than the state-of-the-art classification methods. View Full-Text
Keywords: hyperspectral image (HSI) classification; sparse representation; multi-layer; category sparsity hyperspectral image (HSI) classification; sparse representation; multi-layer; category sparsity

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bian, X.; Chen, C.; Xu, Y.; Du, Q. Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations. Remote Sens. 2016, 8, 985.

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