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Remote Sens. 2016, 8(8), 636; doi:10.3390/rs8080636

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

1,* , 1
and
1,2
1
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Department of Geography, University of Cincinnati (UC), Cincinnati, OH 45221, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 26 April 2016 / Revised: 13 July 2016 / Accepted: 1 August 2016 / Published: 4 August 2016
View Full-Text   |   Download PDF [2426 KB, uploaded 4 August 2016]   |  

Abstract

Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC. View Full-Text
Keywords: hyperspectral image (HSI); classification; tensor; dictionary learning; sparse representation hyperspectral image (HSI); classification; tensor; dictionary learning; sparse representation
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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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He, Z.; Li, J.; Liu, L. Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification. Remote Sens. 2016, 8, 636.

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