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Linear Regression Models and Neural Networks for the Fast Emulation of a Molecular Absorption Code

Guillaume Euvrard 1, * Isabelle Rivals 1 Thierry Huet 2 Sidonie Lefebvre 2 Pierre Simoneau 2 
* Corresponding author
1 ESA - Equipe de Statistique Appliquée (UMRS 1158)
ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris, UMRS 1158 - Neurophysiologie Respiratoire Expérimentale et Clinique : UMRS1158
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 to load or to compute 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 to 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.
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  • HAL Id : hal-00805086, version 1


Guillaume Euvrard, Isabelle Rivals, Thierry Huet, Sidonie Lefebvre, Pierre Simoneau. Linear Regression Models and Neural Networks for the Fast Emulation of a Molecular Absorption Code. Applied optics, Optical Society of America, 2009, 48 (35), pp.6770-6780. ⟨hal-00805086⟩



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