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Hyperspectral image classification using ensemble extreme learning machine based on fuzzy entropy weights and auto-adapted spatial-spectral features

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Abstract

In order to improve the classification of hyperspectral image, we propose a novel hyperspectral image classification model using ensemble extreme learning machine based on fuzzy entropy weights and auto-adapted spatial-spectral features (FEW-ASSELM). To be specific, the fuzzy entropy weight of the band is proposed as a new metric to measure the importance of each band, which can optimize the grouping strategy of the effective bands. With the change of grouping strategy and random training set, the method of auto-adapted spatial-spectral features extraction is proposed to extract the more effective features. According to the characteristics of the suboptimal grouping strategy and auto-adapted spatial-spectral features, the matching framework of ensemble extreme learning machine can be proposed to perform classification operation. Experimental results on the typical hyperspectral image datasets illustrate that the proposed FEW-ASSELM has few adjustable parameters to make the operation simple, and outperforms a variety of the image classification counterparts in terms of the calculation cost and classification accuracy.

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Acknowledgments

This work was supported in part by the Science and Technology Research Project of the Education Department of Liaoning under Grant No. LJ2020QNL013, in part by the Doctoral Initiation Foundation of Liaoning Technical University under Grant No. 19-1026 and Grant No. 21-1050.

Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

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Authors and Affiliations

  1. School of Electrical and Control engineering, Liaoning Technical University, Huludao, China

    Yuping Yin

  2. The Department of basic education, Liaoning Technical University, Huludao, China

    Lin Wei

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  1. Yuping Yin
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  2. Lin Wei
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Correspondence to Lin Wei.

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Yin, Y., Wei, L. Hyperspectral image classification using ensemble extreme learning machine based on fuzzy entropy weights and auto-adapted spatial-spectral features. Multimed Tools Appl 82, 217–238 (2023). https://doi.org/10.1007/s11042-022-13255-7

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  • DOI: https://doi.org/10.1007/s11042-022-13255-7

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