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Hyperspectral image classification via active learning and broad learning system

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Abstract

Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been considered by scholars for the classification of HSIs due to its superior internal structure. Different from the traditional HSI classification mechanism, this paper proposes an active broad learning system approach for HSI classification. The spectral and spatial features of the image are extracted using principal component analysis and local binary patterns, respectively. Then, the vector fusion of the above two features is utilized as the input of the BLS and trained to obtain pre-labels of the samples. The next training samples are selected among the pre-labels by active learning. Unlike other classification algorithms, the method proposed in this paper utilizes active learning (AL) to select high-quality samples for training, thereby reducing the number of samples used and the cost of sample labeling. In addition, the use of incremental learning in broad learning significantly reduces the training time and improves the classification accuracy. The algorithm proposed in this paper is more effective compared to other state-of-the-art algorithms on three HSI datasets.

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Acknowledgements

This work was supported in part by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme, and in part by the National Key Research and Development Program of China under Project 2020AAA0108303.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Huifang Huang, Zhi Liu & Yun Zhang

  2. Faculty of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China

    C. L. Philip Chen

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  1. Huifang Huang
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  2. Zhi Liu
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  3. C. L. Philip Chen
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  4. Yun Zhang
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Correspondence to Zhi Liu.

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Huang, H., Liu, Z., Chen, C.L.P. et al. Hyperspectral image classification via active learning and broad learning system. Appl Intell 53, 15683–15694 (2023). https://doi.org/10.1007/s10489-021-02805-5

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