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Hyperspectral classification employing spatial–spectral low rank representation in hidden fields

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Abstract

This paper presents a novel classification method based on spatial–spectral low-rank representation in the hidden field under a Bayesian framework for hyperspectral imagery. The key idea of the method is to simultaneously explore the low-rank property in the spectral domain and nonlocal self-similarity in the spatial domain of the hidden field, which is estimated by sparse multinomial logistic regression in a supervised manner. First, the low rank property in the spectral domain is exploited in local cubic patches. Following this, similar cubic patches are clustered into several groups in a nonlocal sense and patches in each group are assumed to lie in a low-rank subspace. The final model could be efficiently solved by the augmented Lagrangian method. Experimental results on two real hyperspectral datasets validate that the proposed classifier produces a superior performance compared to other state-of-the-art classifiers in terms of overall accuracy, average accuracy and the kappa statistic (k).

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Acknowledgements

This work was supported by the Natural Science Foundation of Jiangsu Province and China (BK20150923, 61601236, 61602423, 61402235), the PAPD fund and the NRF grants (NRF-2016R1D1A1B03934305, NRF-2017R1A2B2006518) and the Korean Research Fellowship Program (NRF-2015H1D3A1036067) both through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

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Authors and Affiliations

  1. School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China

    Le Sun & Yuhui Zheng

  2. Binjiang College, NUIST, Nanjing, 210044, China

    Shunfeng Wang

  3. College of Information Engineering, Yangzhou University, Yangzhou, 215127, China

    Jin Wang

  4. School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, 440746, South Korea

    Le Sun & Byeungwoo Jeon

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Correspondence to Byeungwoo Jeon.

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Sun, L., Wang, S., Wang, J. et al. Hyperspectral classification employing spatial–spectral low rank representation in hidden fields. J Ambient Intell Human Comput 15, 1505–1516 (2024). https://doi.org/10.1007/s12652-017-0586-1

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