Hyperspectral classification employing spatial–spectral low rank representation in hidden fields
- Authors
- Sun L.; Wang S.; Wang J.; Zheng Y.; Jeon B.
- Issue Date
- Oct-2017
- Publisher
- Springer Verlag
- Keywords
- Hidden field; Hyperspectral classification; Multinomial sparse logistic regression; Spatial–spectral low-rank representation
- Citation
- Journal of Ambient Intelligence and Humanized Computing, pp 1 - 12
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Ambient Intelligence and Humanized Computing
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/32673
- DOI
- 10.1007/s12652-017-0586-1
- ISSN
- 1868-5137
- 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). © 2017 Springer-Verlag GmbH Germany
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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