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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