An improved in-scene atmospheric retrieval and correction algorithm for long-wavelength infrared hyperspectral imagery

Lawrence Bernstein; Jonathan Gelbord; Steven Adler-Golden; Nevzat Guler; Robert Sundberg; Patrick Conforti

doi:10.1117/12.2321116

18 September 2018 An improved in-scene atmospheric retrieval and correction algorithm for long-wavelength infrared hyperspectral imagery

Lawrence Bernstein, Jonathan Gelbord, Steven Adler-Golden, Nevzat Guler, Robert Sundberg, Patrick Conforti

Author Affiliations +

Proceedings Volume 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing; 107680J (2018) https://doi.org/10.1117/12.2321116
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

We describe a new algorithm, QUAC-IR (QUick Atmospheric Correction in the InfraRed), for automated, fast, atmospheric correction of LWIR (Long Wavelength InfraRed) hyperspectral imagery (HSI) and multi-spectral imagery (MSI) in the ~7-14 mm spectral region. QUAC-IR is an in-scene based algorithm, similar to the widely used ISAC (In- Scene Atmospheric Correction) algorithm. It improves upon the ISAC approach in several key ways, including providing absolute, versus relative, sensor-to-ground transmittances and radiances, as well as an estimate of the atmospheric downwelling sky radiance. The latter is important for retrieving emissivity from a reflective (i.e., non-blackbody) pixel. The key aspect of QUAC-IR is that it explicitly searches for blackbody pixels using an efficient approach involving a small number of spectral channels in which the atmospheric radiative transfer is dominated by the water continuum. This allows for fast and simplified Beer's Law (i.e., exponential) scaling of the path transmittance and radiance based on a compact library of pre-computed reference values. We apply QUAC-IR to well-calibrated data from the SEABASS¹ and MAKO² HSI sensors. The results are compared to those from a first-principles physics-based atmospheric code, FLAASH-IR.

Conference Presentation

Citation Download Citation

Lawrence Bernstein, Jonathan Gelbord, Steven Adler-Golden, Nevzat Guler, Robert Sundberg, and Patrick Conforti "An improved in-scene atmospheric retrieval and correction algorithm for long-wavelength infrared hyperspectral imagery", Proc. SPIE 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing, 107680J (18 September 2018); https://doi.org/10.1117/12.2321116

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