Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imaging

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dc.contributor.advisor Schott, John en_US
dc.contributor.advisor Kandlikar, S. en_US
dc.contributor.advisor Messinger, David en_US
dc.contributor.advisor Salvaggio, Carl en_US
dc.contributor.author Boonmee, Marvin en_US
dc.date.accessioned 2008-03-10T17:11:26Z
dc.date.available 2008-03-10T17:11:26Z
dc.date.issued 2007-10 en_US
dc.identifier.uri http://hdl.handle.net/1850/5868
dc.description.abstract A new algorithm, optimized land surface temperature and emissivity retrieval (OLSTER), is presented to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve properties of both natural and man-made materials. Multi-directional or multi-temporal observations are not required, and the scenes do not have to be dominated by blackbody features. The OLSTER algorithm consists of a preprocessing step, an iterative search for nearblackbody pixels, and an iterative constrained optimization loop. The preprocessing step provides initial estimates of LST per pixel and the atmospheric parameters of transmittance and upwelling radiance for the entire image. Pixels that are under- or overcompensated by the estimated atmospheric parameters are classified as near-blackbody and lower emissivity pixels, respectively. A constrained optimization of the atmospheric parameters using generalized reduced gradients on the near-blackbody pixels ensures physical results. The downwelling radiance is estimated from the upwelling radiance by applying a look-up table of coefficients based on a polynomial regression of radiative transfer model runs for the same sensor altitude. The LST and emissivity per pixel are retrieved simultaneously using the well established ISSTES algorithm. The OLSTER algorithm retrieves land surface temperatures within about ± 1.0 K, and emissivities within about ± 0.01 based on numerical simulation and validation work comparing results from sensor data with ground truth measurements. The OLSTER algorithm is currently one of only a few algorithms available that have been documented to retrieve accurate land surface temperatures and absolute land surface spectral emissivities from passive airborne hyperspectral LWIR sensor imagery. en_US
dc.language.iso en_US en_US
dc.subject Atmospheric compensation en_US
dc.subject GRG en_US
dc.subject Hyperspectral en_US
dc.subject Land surface emissivity en_US
dc.subject Land surface temperature en_US
dc.subject LSE en_US
dc.subject LST en_US
dc.subject LWIR en_US
dc.subject Nonlinear optimization en_US
dc.subject OLSTER en_US
dc.subject Temperature/emissivity separation en_US
dc.subject Thermal infrared en_US
dc.subject TIR en_US
dc.subject.lcc QE511 .B66 2007
dc.subject.lcsh Earth temperature--Remote sensing en_US
dc.subject.lcsh Earth temperature--Mathematical models en_US
dc.subject.lcsh Remote sensing--Data processing en_US
dc.subject.lcsh Image processing--Digital techniques en_US
dc.title Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imaging en_US
dc.type Thesis en_US
dc.description.college College of Imaging Arts and Sciences en_US
dc.description.department Chester F. Carlson Center for Imaging Science en_US

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