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Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble

[+] Author Affiliations
Puhua Chen, Licheng Jiao, Jiaqi Zhao, Zhiqiang Zhao

Xidian University, International Research Center for Intelligent Perception and Computation, Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, No. 2 South Taibai Road, Xi’an, Shaanxi Province 710071, China

Fang Liu

Xidian University, International Research Center for Intelligent Perception and Computation, School of Computer Science and Technology, Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, No. 2 South Taibai Road, Xi’an, Shaanxi Province 710071, China

J. Appl. Remote Sens. 10(3), 030501 (Jul 15, 2016). doi:10.1117/1.JRS.10.030501
History: Received January 27, 2016; Accepted June 20, 2016
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Abstract.  Hyperspectral data are the spectral response of landcovers from different spectral bands and different band sets can be treated as different views of landcovers, which may contain different structure information. Therefore, multiview graphs ensemble-based graph embedding is proposed to promote the performance of graph embedding for hyperspectral image classification. By integrating multiview graphs, more affluent and more accurate structure information can be utilized in graph embedding to achieve better results than traditional graph embedding methods. In addition, the multiview graphs ensemble-based graph embedding can be treated as a framework to be extended to different graph-based methods. Experimental results demonstrate that the proposed method can improve the performance of traditional graph embedding methods significantly.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Puhua Chen ; Licheng Jiao ; Fang Liu ; Jiaqi Zhao and Zhiqiang Zhao
"Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble", J. Appl. Remote Sens. 10(3), 030501 (Jul 15, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.030501


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