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.

Library of Congress Subject Headings

Earth temperature--Remote sensing; Earth temperature--Mathematical models; Remote sensing--Data processing; Image processing--Digital techniques

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Schott, John

Advisor/Committee Member

Kandlikar, S.

Advisor/Committee Member

Messinger, David


Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: QE511 .B66 2007


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