Special Section on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis

Locality-preserving sparse representation-based classification in hyperspectral imagery

[+] Author Affiliations
Lianru Gao, Bing Zhang, Qingting Li

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Haoyang Yu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

J. Appl. Remote Sens. 10(4), 042004 (Jun 17, 2016). doi:10.1117/1.JRS.10.042004
History: Received January 26, 2016; Accepted May 26, 2016
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Abstract.  This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Lianru Gao ; Haoyang Yu ; Bing Zhang and Qingting Li
"Locality-preserving sparse representation-based classification in hyperspectral imagery", J. Appl. Remote Sens. 10(4), 042004 (Jun 17, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042004


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