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Journal of Applied Remote Sensing

Atmospheric compensation in the presence of clouds: an adaptive empirical line method (AELM) approach
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Paper Abstract

Many algorithms exist to invert airborne imagery from units of either radiance or sensor specific digital counts to units of reflectance. These compensation algorithms remove unwanted atmospheric variability allowing objects on the ground to be analyzed. Low error levels in homogenous atmospheric conditions have been demonstrated. In many cases however, clouds are present in the atmosphere which introduce error into the inversion at unacceptable levels. For example, the relationship that is defined between sensor reaching radiance and ground reflectance in a cloud free scene will not be the same as in an adjacent region with clouds in the surround. A novel method has been developed which utilizes ground based measurements to modify the empirical line method (ELM) approach on a per-pixel basis. A physics based model of the atmosphere is used to generate a spatial correction for the ELM. Creation of this model is accomplished by analyzing whole-sky imagery to produce a cloud mask which drives input parameters to the radiative transfer (RT) code MODTRAN. The RT code is run for several different azimuth and zenith orientations to create a three-dimensional representation of the hemisphere. The model is then used to achieve a per-pixel correction by adjusting the ELM slope spatially. This method is applied to real data acquired over the atmospheric radiation measurement (ARM) site in Lamount, OK. Performance of the method is evaluated with the Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument. The sensitivity to spectral sampling is also assessed by down-sampling the HYDICE data to the spectral response of the multi-spectral system Wildfire Airborne Sensor Program LITE (WASP Lite). Finally a method to utilize this approach when additional sensors (like a sky camera) are not available is suggested.

Paper Details

Date Published: 1 February 2009
PDF: 16 pages
J. Appl. Remote Sens. 3(1) 033507 doi: 10.1117/1.3091937
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Brent D. Bartlett, Rochester Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)


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