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Proceedings Paper

Higher-dimensional wavelet transforms for hyperspectral data compression and feature recognition
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Paper Abstract

The dominant image processing tasks for hyperspectral data are compression and feature recognition. These tasks go hand-in-hand. Hyperspectral data contains a huge amount of information that need to be processed (and often very quickly) depending on the application. The discrete wavelet transform is the ideal tool for this type of data structure. There are applications that require such processing (especially feature recognition or identification) be done extremely fast and efficiently. Furthermore the higher number of dimensions implies a number of different ways to do these transforms. Much of the work in this area to the present time has been focused on JPEG2000 type compression of each component image involving fairly sophisticated coding techniques; relatively little attention has been paid to other configurations of wavelet transforms of such data, as well as rapid feature identification where compression may not be necessary at all. This paper describes other versions of the 3D wavelet transform that allow the resolution in both the spatial domain and spectral domain to be adjusted separately. Other issues associated with low complexity feature recognition with and without compression using versions of the 3D hyperspectral wavelet transforms will be discussed along with some illustrative calculations.

Paper Details

Date Published: 28 January 2004
PDF: 12 pages
Proc. SPIE 5208, Mathematics of Data/Image Coding, Compression, and Encryption VI, with Applications, (28 January 2004); doi: 10.1117/12.508086
Show Author Affiliations
James F. Scholl, Optical Sciences Ctr./Univ. of Arizona (United States)
Eustace L. Dereniak, Optical Sciences Ctr./Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 5208:
Mathematics of Data/Image Coding, Compression, and Encryption VI, with Applications
Mark S. Schmalz, Editor(s)

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