Improved atmospheric characterization for hyperspectral exploitation

Nathan P. Wurst; Joseph Meola; Steven T. Fiorino

doi:10.1117/12.2265853

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5 May 2017 Improved atmospheric characterization for hyperspectral exploitation

Nathan P. Wurst, Joseph Meola, Steven T. Fiorino

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Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101980B (2017) https://doi.org/10.1117/12.2265853
Event: SPIE Defense + Security, 2017, Anaheim, California, United States

Abstract

Airborne hyperspectral imaging (HSI)has shown utility in material detection and identification. Recent interest in longwave infrared (LWIR) HSI systems operating in the 7-14 micron range has developed due to strong spectral features of minerals, chemicals, and gaseous effluents. LWIR HSI has the advantage over other spectral bands by operating in day or night scenarios because emitted/reflected thermal radiation rather than reflected sunlight is measured. This research seeks to determine the most effective methods to perform model-based atmospheric compensation (AC) of LWIR HSI data using two existing atmospheric radiative transfer (RT) models, MODTRAN and LEEDR. MODTRAN is the more established RT model, but it lacks LEEDRs robust capability to generate realistic atmospheric profiles from probabilistic climatology or observations and forecasts from numerical weather prediction (NWP) models. The advantage of LEEDR’s ability to generate atmospheres is tested by using LEEDR atmospheres, a MODTRAN standard model, and radiosonde data to perform AC on an airborne hyperspectral datacube with nadir looking geometry. This work investigates the potential benefit of LEEDR’s weather/climatology tools for improving and/or expediting the AC process for LWIR HSI.

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Nathan P. Wurst, Joseph Meola, and Steven T. Fiorino "Improved atmospheric characterization for hyperspectral exploitation", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980B (5 May 2017); https://doi.org/10.1117/12.2265853

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