Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery

Lawrence S. Bernstein; Steven M. Adler-Golden; Robert L. Sundberg; Robert Y. Levine; Timothy C. Perkins; Alexander Berk; Anthony J. Ratkowski; Gerald Felde; Michael L. Hoke

doi:10.1117/12.603359

1 June 2005 Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery

Lawrence S. Bernstein, Steven M. Adler-Golden, Robert L. Sundberg, Robert Y. Levine, Timothy C. Perkins, Alexander Berk, Anthony J. Ratkowski, Gerald Felde, Michael L. Hoke

Author Affiliations +

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.603359
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.

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Lawrence S. Bernstein, Steven M. Adler-Golden, Robert L. Sundberg, Robert Y. Levine, Timothy C. Perkins, Alexander Berk, Anthony J. Ratkowski, Gerald Felde, and Michael L. Hoke "Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.603359

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