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7 May 2007 Spectral/spatial filter selection for illumination-invariant hyperspectral texture discrimination
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We propose a method for selecting an optimal spatial filter based on both spectral and spatial information to improve the discriminability of hyperspectral textures. The feature vector for each texture class contains the covariance matrix elements in filtered versions of the texture. The new method reduces the length of the representation by selecting an optimal subset of bands and also uses an optimized spatial filter to maximize the distance between feature vectors for the different texture classes. Band selection is performed based on the stepwise reduction of bands. We have applied this method to a database of textures acquired under different illumination conditions and analyzed the classification results.
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Negar Nejati and Glenn Healey "Spectral/spatial filter selection for illumination-invariant hyperspectral texture discrimination", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650I (7 May 2007);

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