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Journal of Applied Remote Sensing

Locality-preserving sparse representation-based classification in hyperspectral imagery
Author(s): Lianru Gao; Haoyang Yu; Bing Zhang; Qingting Li
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Paper 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.

Paper Details

Date Published: 17 June 2016
PDF: 15 pages
J. Appl. Rem. Sens. 10(4) 042004 doi: 10.1117/1.JRS.10.042004
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Haoyang Yu, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)
Qingting Li, Institute of Remote Sensing and Digital Earth (China)

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