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Proceedings Paper

Wavelet packets for multi- and hyper-spectral imagery
Author(s): J. J. Benedetto; W. Czaja; M. Ehler; C. Flake; M. Hirn
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Paper Abstract

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

Paper Details

Date Published: 4 February 2010
PDF: 11 pages
Proc. SPIE 7535, Wavelet Applications in Industrial Processing VII, 753508 (4 February 2010); doi: 10.1117/12.843039
Show Author Affiliations
J. J. Benedetto, Univ. of Maryland, College Park (United States)
W. Czaja, Univ. of Maryland, College Park (United States)
M. Ehler, Univ. of Maryland, College Park (United States)
C. Flake, Univ. of Maryland, College Park (United States)
M. Hirn, Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 7535:
Wavelet Applications in Industrial Processing VII
Frédéric Truchetet; Olivier Laligant, Editor(s)

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