Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble

Puhua Chen; Licheng Jiao; Fang Liu; Jiaqi Zhao; Zhiqiang Zhao

doi:10.1117/1.JRS.10.030501

15 July 2016 Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble

Puhua Chen, Licheng Jiao, Fang Liu, Jiaqi Zhao, Zhiqiang Zhao

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 030501 (July 2016). https://doi.org/10.1117/1.JRS.10.030501

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.

Citation Download Citation

Puhua Chen, Licheng Jiao, Fang Liu, Jiaqi Zhao, and Zhiqiang Zhao "Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble," Journal of Applied Remote Sensing 10(3), 030501 (15 July 2016). https://doi.org/10.1117/1.JRS.10.030501

Published: 15 July 2016

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