Linear regression models and neural networks for the fast emulation of a molecular absorption code
Applied Optics, Vol. 48, Issue 35, pp. 6770-6780 (2009)
http://dx.doi.org/10.1364/AO.48.006770
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Abstract
The background scene generator MATISSE, whose main functionality is to generate natural background radiance images, makes use of the so-called Correlated K (CK) model. It necessitates either loading or computing thousands of CK coefficients for each atmospheric profile. When the CK coefficients cannot be loaded, the computation time becomes prohibitive. The idea developed in this paper is to substitute fast approximate models for the exact CK generator; using the latter, a representative set of numerical examples is built and used to train linear or nonlinear regression models. The resulting models enable an accurate CK coefficient computation for all the profiles of an image in a reasonable time.
© 2009 Optical Society of America
OCIS Codes
(000.4430) General : Numerical approximation and analysis
(010.1300) Atmospheric and oceanic optics : Atmospheric propagation
(110.2960) Imaging systems : Image analysis
(200.4260) Optics in computing : Neural networks
(010.1030) Atmospheric and oceanic optics : Absorption
(010.5620) Atmospheric and oceanic optics : Radiative transfer
ToC Category:
Atmospheric and Oceanic Optics
History
Original Manuscript: July 22, 2009
Revised Manuscript: October 16, 2009
Manuscript Accepted: October 23, 2009
Published: December 3, 2009
Citation
Guillaume Euvrard, Isabelle Rivals, Thierry Huet, Sidonie Lefebvre, and Pierre Simoneau, "Linear regression models and neural networks for the fast emulation of a molecular absorption code," Appl. Opt. 48, 6770-6780 (2009)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-48-35-6770
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