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Proceedings Paper

Fast feature extraction in hyperspectral imagery via lifting wavelet transforms
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Paper Abstract

Feature extraction from hyperspectral imagery consumes large amounts of memory. Hence the algorithms to do this have high computational complexity and require large amounts of additional computer memory. To address these issues previous work has concentrated on algorithms that are combinations of a fast integer-based hyperspectral discrete wavelet transform (HSDWT) with a specialized implementation of the Haar basis and improved implementations of linear spectral unmixing. Extensions of that previous work are presented here that modify and extend these algorithms to investigate feature extraction of arbitrary shaped spatial regions and incorporate more general biorthogonal bases for processing of spectral signatures. Finally, these wavelet transform implementations have also been used to simulate linear spectral unmixng techniques on spatially unresolved objects such as binary stars and globular star clusters.

Paper Details

Date Published: 17 September 2005
PDF: 12 pages
Proc. SPIE 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, 59150N (17 September 2005); doi: 10.1117/12.613327
Show Author Affiliations
James F. Scholl, College of Optical Sciences/Univ. of Arizona (United States)
Oceanit Labs., Inc. (United States)
Eustace L. Dereniak, College of Optical Sciences/Univ. of Arizona (United States)
E. Keith Hege, MKS Imaging Technology, LLC (United States)
Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 5915:
Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications
Mark S. Schmalz, Editor(s)

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