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• W4320732076 startingPage "2129" @default.
• W4320732076 abstract "Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ignoring the diversity of subsets. Moreover, the ordered property in HSI is expected to be focused in order to avoid choosing redundant bands. In this paper, we proposed an unsupervised band selection method based on the multimodal evolutionary algorithm and subspace decomposition to alleviate the problems. To explore the diversity of band subsets, the multimodal evolutionary algorithm is first employed in spectral subspace decomposition to seek out multiple global or local solutions. Meanwhile, in view of ordered property, we concentrate more on increasing the difference between neighbor band subspaces. Furthermore, to utilize the obtained multiple diverse band subsets, an integrated utilization strategy is adopted to improve the predicted performance. Experimental results on three popular hyperspectral remote sensing datasets and one collected composition prediction dataset show the effectiveness of the proposed method, and the superiority over state-of-the-art methods on predicted accuracy." @default.
• W4320732076 created "2023-02-15" @default.
• W4320732076 creator A5019297753 @default.
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• W4320732076 date "2023-02-14" @default.
• W4320732076 modified "2023-10-06" @default.
• W4320732076 title "Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition" @default.
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• W4320732076 doi "https://doi.org/10.3390/s23042129" @default.
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• W4320732076 hasPublicationYear "2023" @default.
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