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2014•Unpublished venueRequires access

Remote sensing of surface emissivity with the telops Hyper-Cam

Steven M. Adler‐Golden, Patrick F. Conforti, Marc-André Gagnon, Pierre Tremblay, Martin Chamberland

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Abstract

Processing long-wave infrared (LWIR) hyperspectral imagery to surface spectral emissivity or reflectance units via atmospheric compensation and temperature-emissivity separation (TES) affords the opportunity to remotely classify and identify surface materials with minimal interference from atmospheric effects. This paper describes an automated atmospheric compensation and TES method, called FLAASH-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes - Infrared), and its application to airborne imagery taken with the Telops Inc. Hyper-Cam interferometric hyperspectral imager. The results demonstrate good suppression of the atmospheric features due to water vapor and ozone, resulting in quantitative surface spectra, even with highly reflective (low emissivity) objects such as bare metal.

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What this paper is about

Processing long-wave infrared (LWIR) hyperspectral imagery to surface spectral emissivity or reflectance units via atmospheric compensation and temperature-emissivity separation (TES) affords the opportunity to remotely classify and identify surface materials with minimal interference from atmospheric effects. This paper describes an automated atmospheric compensation and TES method, called FLAASH-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes - Infrared), and its application to airborne imagery taken with the Telops Inc. Hyper-Cam interferometric hyperspectral imager. The results demonstrate good suppression of the atmospheric features due to water vapor and ozone, resulting in quantitative surface spectra, even with highly reflective (low emissivity) objects such as bare metal.

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Available abstract

Processing long-wave infrared (LWIR) hyperspectral imagery to surface spectral emissivity or reflectance units via atmospheric compensation and temperature-emissivity separation (TES) affords the opportunity to remotely classify and identify surface materials with minimal interference from atmospheric effects. This paper describes an automated atmospheric compensation and TES method, called FLAASH-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes - Infrared), and its application to airborne imagery taken with the Telops Inc. Hyper-Cam interferometric hyperspectral imager. The results demonstrate good suppression of the atmospheric features due to water vapor and ozone, resulting in quantitative surface spectra, even with highly reflective (low emissivity) objects such as bare metal.

Key concepts: Emissivity, Hyperspectral imaging, Remote sensing, Atmospheric correction, Environmental science, Infrared, Infrared window, Spectral resolution

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