27 April 2009 Rotation and scale invariant hyperspectral classification using 3D Gabor filters
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Abstract
We use a bank of three-dimensional Gabor filters to represent the spectral/spatial properties of hyperspectral data. The orientation and scale selective properties of the filters allow the development of new algorithms that are invariant to rotation and scale. Since a large set of three-dimensional filters can be defined, we develop methods for reducing the number of features that are used to represent a region. The data reduction process is defined to optimize the features for classification. We demonstrate the efficacy of the approach using a large set of AVIRIS hyperspectral data.
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Tien C. Bau, Tien C. Bau, Glenn Healey, Glenn Healey, } "Rotation and scale invariant hyperspectral classification using 3D Gabor filters", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340B (27 April 2009); doi: 10.1117/12.819075; https://doi.org/10.1117/12.819075
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