Rotation and scale invariant hyperspectral classification using 3D Gabor filters

Tien C. Bau; Glenn Healey

doi:10.1117/12.819075

27 April 2009 Rotation and scale invariant hyperspectral classification using 3D Gabor filters

Tien C. Bau, Glenn Healey

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73340B (2009) https://doi.org/10.1117/12.819075
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Tien C. Bau and 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); https://doi.org/10.1117/12.819075

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