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Comparison of longwave infrared hyperspectral target detection methods
Author(s): Nathan P. Wurst; Seung Hwan An; Joseph Meola
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Paper Abstract

Numerous methods exist to perform hyperspectral target detection. Application of these algorithms often requires the data to be atmospherically corrected. Detection for longwave infrared data typically requires surface temperature estimates as well. This work compares the relative robustness of various target detection algorithms with respect to atmospheric compensation and target temperature uncertainty. Specifically, the adaptive coherence estimator and spectral matched filter will be compared with subspace detectors for various methods of atmospheric compensation and temperature-emissivity separation. Comparison is performed using both daytime and nighttime longwave infrared hyperspectral data collected at various altitudes for various target materials.

Paper Details

Date Published: 14 May 2019
PDF: 12 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 1098617 (14 May 2019); doi: 10.1117/12.2518638
Show Author Affiliations
Nathan P. Wurst, Air Force Research Lab. (United States)
Seung Hwan An, Air Force Research Lab. (United States)
Joseph Meola, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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