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Proceedings Paper

Atmospheric correction of commercial thermal infrared hyperspectral imagery using FLAASH-IR
Author(s): Steven Adler-Golden; Nevzat Guler; Timothy Perkins
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Paper Abstract

Within the last few years, several commercial long-wave infrared (LWIR) hyperspectral imaging (HSI) systems have been developed for remote sensing of the ground from aircraft. While much less expensive and more practical to operate than sensors such as SEBASS and MAKO, which have been developed primarily for research and Government use, the commercial systems have poorer signal-to-noise and/or spectral resolution. We investigate the utility of three commercial systems—the Telops Hyper-Cam, SPECIM AisaOWL, and ITRES TASI-600—for quantitative retrieval of surface temperature and emissivity spectra. Atmospheric retrieval, correction and temperature-emissivity separation are performed on example data from these sensors using FLAASH-IR, a first-principles algorithm that incorporates radiation transport calculations and atmosphere models from MODTRAN. The results from the commercial sensors are noisy compared with SEBASS but otherwise appear to be reasonable. Applying a noise suppression algorithm to the radiance data yields better temperature retrievals and much cleaner emissivity spectra, with minimal loss of information, and should benefit scene classification applications.

Paper Details

Date Published: 8 May 2018
PDF: 7 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440U (8 May 2018); doi: 10.1117/12.2305146
Show Author Affiliations
Steven Adler-Golden, Spectral Sciences, Inc. (United States)
Nevzat Guler, Spectral Sciences, Inc. (United States)
Timothy Perkins, Spectral Sciences, Inc. (United States)

Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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