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Target detection from hyperspectral imagery requires the fusion of information from hundreds of spectral bands. In this paper, we study such fusion in the context of hyperspectral image classification. Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive(More)
Hyperspectral imagery consists of hundreds of spectra or bands whose intensity is measured at various wavelength. Fusing the multiple spectral bands can provide more potential to differentiate between natural and man-made objects, and significantly improve the capability of target detection and classification. Spectral band or wavelength selection is one of(More)
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