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• W2001084846 abstract "The typical characteristics of hyperspectral remote sensing data are combining image with spectrum, high spectral resolution, many bands and much redundant information. A hyperspectral image cube can be used to obtain millions spectrum curves. Aiming at a hyperspectral remote sensing image containing huge amounts of data, removing redundant information and reducing processing dimensions are the premise and foundation for hyperspectral remote sensing processing and applications. Hyperspectral data dimension reduction techniques mainly include the feature extraction and band selection. This paper has fully studied the theories and methods of dimension reduction for band selection, analyses their advantages, disadvantages and validity, and deeply discusses the current situation and tendency of the development of band selection based on dimension reduction of hyperspectral remote sensing image at last." @default.
• W2001084846 created "2016-06-24" @default.
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• W2001084846 date "2012-06-01" @default.
• W2001084846 modified "2023-10-17" @default.
• W2001084846 title "Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images" @default.
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• W2001084846 doi "https://doi.org/10.1109/rsete.2012.6260684" @default.
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