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Proceedings Paper

Detecting low-emissivity objects in LWIR hyperspectral data and the corresponding impact on atmospheric compensation
Author(s): Robert Daniel Kaiser; David Lee Vititoe; Aaron Keith Andrews
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Paper Abstract

Several of the leading atmospheric compensation algorithms for LWIR hyperspectral data require the detection and exclusion low-emissivity objects from the analysis. In this paper, nine different methods for detection of low-emissivity objects are presented. In testing, it was found that the algorithms proposed suffered from temperature sensitivities. Further testing was accomplished without filtering to test the performance of Scaled and Unscaled ISAC under a range of environmental and system parameters. Detection performance is quantified directly in terms of probability of detection vs. probability of false alarm and in terms of atmospheric state parameters.

Paper Details

Date Published: 23 September 2003
PDF: 14 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.497122
Show Author Affiliations
Robert Daniel Kaiser, Harris Corp. (United States)
David Lee Vititoe, Harris Corp. (United States)
Aaron Keith Andrews, Harris Corp. (United States)


Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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