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Proceedings Paper

Rotation and scale invariant hyperspectral classification using 3D Gabor filters
Author(s): Tien C. Bau; Glenn Healey
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Paper 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.

Paper Details

Date Published: 27 April 2009
PDF: 13 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340B (27 April 2009); doi: 10.1117/12.819075
Show Author Affiliations
Tien C. Bau, Univ. of California, Irvine (United States)
Glenn Healey, Univ. of California, Irvine (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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