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The rich spectral information in hyperspectral imagery gives rise to huge storage and transmission costs. Dimensionality reduction aims to reduce the space complexity in hyperspectral imagery by projecting data into a low-dimensional subspace. There has been an increasing interest in dimensionality reduction driven by random projections due to its data-independent representation as well as desirable...
There is increasing interest in driving supervised classification of hyperspectral imagery by a support vector machine using a composite kernel employing both spectral and spatial features. While the spectral signature of the current hyper-spectral pixel is often used directly to supply the spectral feature, a statistic — such as the mean — calculated across a spatial window surrounding the pixel...
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