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Hyperspectral remote sensing image classification with information discriminative extreme learning machine

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Abstract

Hyperspectral remote sensing image classification is important aspect of current research. Extreme learning machine (ELM) has been widely used in the field of pattern recognition for its efficient and good generalization performance. With the study of hyperspectral remote sensing image classification, this paper proposes an information discriminative extreme learning machine (IELM). IELM inherits the advantages of ELM, can solve the problems that ELM learning is insufficient for hyperspectral remote sensing image with limited scale of sample data. The proposed algorithm is tested by experiments for hyperspectral remote sensing image classification. The experiment results show that the proposed algorithm has better classification effect.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (61105085) and Science Foundation of education ministry of Liaoning province (L2014427).

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116081, China

    Deqin Yan, Yonghe Chu, Lina Li & Deshan Liu

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  1. Deqin Yan
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  2. Yonghe Chu
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  3. Lina Li
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  4. Deshan Liu
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Correspondence to Deqin Yan.

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Yan, D., Chu, Y., Li, L. et al. Hyperspectral remote sensing image classification with information discriminative extreme learning machine. Multimed Tools Appl 77, 5803–5818 (2018). https://doi.org/10.1007/s11042-017-4494-3

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  • DOI: https://doi.org/10.1007/s11042-017-4494-3

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