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A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

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Abstract

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. Whale Optimization Algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

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Funding

This work was funded by the National Natural Science Foundation of China under Grant No.41901296, 41925007, 41901285, the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation under Grant No.2018NGCM06, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant No.2642019046.

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Authors and Affiliations

  1. Institute of Geological Survey, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Mingwei Wang & Ziqi Yan

  2. Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan, 430079, People’s Republic of China

    Mingwei Wang

  3. Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430079, People’s Republic of China

    Jianwei Luo

  4. School of Computer Science, Hubei University of Technology, Wuhan, 430068, People’s Republic of China

    Zhiwei Ye

  5. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450045, People’s Republic of China

    Peipei He

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  1. Mingwei Wang
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Corresponding author

Correspondence to Jianwei Luo.

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Mingwei Wang and Ziqi Yan declare that they have no conflict of interest. Jianwei Luo declares that he has no conflict of interest. Zhiwei Ye declares that he has no conflict of interest. Peipei He declares that she has no conflict of interest.

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Wang, M., Yan, Z., Luo, J. et al. A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image. Appl Intell 51, 7766–7780 (2021). https://doi.org/10.1007/s10489-021-02270-0

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