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Remote Sens. 2017, 9(5), 452;

Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China
College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029,China
Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 14 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [1335 KB, uploaded 8 May 2017]


Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA) is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods. View Full-Text
Keywords: hyperspectral image; sparse and low-rank graph; tensor; dimensionality reduction hyperspectral image; sparse and low-rank graph; tensor; dimensionality reduction

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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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Pan, L.; Li, H.-C.; Deng, Y.-J.; Zhang, F.; Chen, X.-D.; Du, Q. Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis. Remote Sens. 2017, 9, 452.

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