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Proceedings Paper

Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors
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Paper Abstract

The linear mixing model is widely used in hyperspectral imaging applications to model the reflectance spectra of mixed pixels in the SWIR atmospheric window or the radiance spectra of plume gases in the LWIR atmospheric window. In both cases it is important to detect the presence of materials or gases and then estimate their amount, if they are present. The detection and estimation algorithms available for these tasks are related but they are not identical. The objective of this paper is to theoretically investigate how the heavy tails observed in hyperspectral background data affect the quality of abundance estimates and how the F-test, used for endmember selection, is robust to the presence of heavy tails when the model fits the data.

Paper Details

Date Published: 15 October 2004
PDF: 6 pages
Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); doi: 10.1117/12.559496
Show Author Affiliations
Dimitris G. Manolakis, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 5546:
Imaging Spectrometry X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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