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Proceedings Paper

Hyperspectral feature classification with alternate wavelet transform representations
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Paper Abstract

The effectiveness of many hyperspectral feature extraction algorithms involving classification (and linear spectral unmixing) are dependent on the use of spectral signature libraries. If two or more signatures are roughly similar to each other, these methods which use algorithms such as singular value decomposition (SVD) or least squares to identify the object will not work well. This especially goes for these procedures which are combined with three-dimensional discrete wavelet transforms, which replace the signature libraries with their corresponding lowpass wavelet transform coefficients. In order to address this issue, alternate ways of transforming these signature libraries using bandpass or highpass wavelet transform coefficients from either wavelet or Walsh (Haar wavelet packet) transforms in the spectral direction will be described. These alternate representations of the data emphasize differences between the signatures which lead to improved classification performance as compared to existing procedures.

Paper Details

Date Published: 25 August 2006
PDF: 12 pages
Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 63150G (25 August 2006); doi: 10.1117/12.682583
Show Author Affiliations
James F. Scholl, College of Optical Sciences, Univ. of Arizona (United States)
E. Keith Hege, MKS Imaging Technology, LLC (United States)
Steward Observatory, Univ. of Arizona (United States)
Michael Lloyd-Hart, Steward Observatory, Univ. of Arizona (United States)
Eustace L. Dereniak, College of Optical Sciences, Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 6315:
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
Gerhard X. Ritter; Mark S. Schmalz; Junior Barrera; Jaakko T. Astola, Editor(s)

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