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Parametric 3D atmospheric reconstruction in highly variable terrain with recycled Monte Carlo Paths and an adapted bayesian inference engine

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dc.contributor.author Langmore, Ian
dc.contributor.author Davis, Anthony B.
dc.contributor.author Bal, Guillaume
dc.contributor.author Marzouk, Youssef M.
dc.date.accessioned 2013-06-21T17:54:40Z
dc.date.available 2013-06-21T17:54:40Z
dc.date.issued 2012-08-06
dc.identifier.citation International Radiation Symposium 2012 (IRS2012), Berlin, Germany, August 06-10, 2012 en_US
dc.identifier.clearanceno 12-4699
dc.identifier.uri http://hdl.handle.net/2014/43300
dc.description.abstract We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single lowresolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as “multi-pixel” techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2012. en_US
dc.subject remote sensting en_US
dc.subject multi-pixel techniques en_US
dc.subject 3-D radiative transfer en_US
dc.subject Monte Carlo en_US
dc.subject Bayesian methods en_US
dc.title Parametric 3D atmospheric reconstruction in highly variable terrain with recycled Monte Carlo Paths and an adapted bayesian inference engine en_US
dc.type Preprint en_US


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