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We use Gabor filters to extract texture features at different scales and orientations from hyperspectral images. The texture features are derived from both individual bands and combinations of bands. We consider both spectral binning and principal components analysis for reducing the dimensionality of the input data. Using a database of Airborne Visible Infrared Imaging Spectrometer image regions, we evaluate the performance of this approach for recognizing hyperspectral textures. We show that opponent features that consider combinations of spectral bands often help improve performance. We also examine the dependence of recognition performance on the dimensionality reduction strategy and the number of spectral bands.