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Proceedings Paper

Effects of dimensionality reduction on the statististical distribution of hyperspectral backgrounds
Author(s): M. Rossacci; D. Manolakis; J. Cipar; R. Lockwood; T. Cooley; J. Jacobson
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Paper Abstract

The objective of this paper is to investigate the effects of dimensionality reduction on the statistical distribution of natural hyperspectral backgrounds. The statistical modeling is based on application of the multivariate t-elliptically contoured distribution to background regions which have been shown to exhibit "long-tail" behavior. Hyperspectral backgrounds are commonly represented with reduced dimensionality in order to minimize statistical redundancies in the spectral dimension and to satisfy data processing and storage requirements. In this investigation, we extend the statistical characterization of these backgrounds by modeling their Mahalanobis distance distributions in reduced dimensional space. The dimensionality reduction techniques applied in this paper include Principal Components Analysis (PCA) and spectral band aggregation. The knowledge gained from a better understanding of the effects of dimensionality reduction will be beneficial toward improving threshold selection for target detection applications. These investigations are done using hyperspectral data from the AVIRIS sensor and include spectrally homogeneous regions of interest obtained by visual interactive spatial segmentation.

Paper Details

Date Published: 1 September 2006
PDF: 16 pages
Proc. SPIE 6302, Imaging Spectrometry XI, 63020H (1 September 2006); doi: 10.1117/12.680388
Show Author Affiliations
M. Rossacci, MIT Lincoln Lab. (United States)
D. Manolakis, MIT Lincoln Lab. (United States)
J. Cipar, Air Force Research Lab. (United States)
R. Lockwood, Air Force Research Lab. (United States)
T. Cooley, Air Force Research Lab. (United States)
J. Jacobson, National Air and Space Intelligence Ctr. (United States)

Published in SPIE Proceedings Vol. 6302:
Imaging Spectrometry XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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