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Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

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  • Jing Liu
  • Yulong Qiao

Abstract

Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance–based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix–based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance–based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance–based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing.

Suggested Citation

  • Jing Liu & Yulong Qiao, 2020. "Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:11:p:1550147720968467
    DOI: 10.1177/1550147720968467
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    References listed on IDEAS

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    1. Ai-Qing Tian & Shu-Chuan Chu & Jeng-Shyang Pan & Huanqing Cui & Wei-Min Zheng, 2020. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
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    Cited by:

    1. Chi-Hua Chen & Kuo-Ming Chao & Feng-Jang Hwang & Chunjia Han & Lianrong Pu, 2021. "Editorial," International Journal of Distributed Sensor Networks, , vol. 17(2), pages 15501477219, February.

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