Atmospheric inversion in the presence of clouds: an adaptive ELM approach

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 a scene with clouds. 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 as well as a simulated multi-spectral system.