@article{Bartlett2009_1,<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Abstract = {Many methods 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. 
},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Author = {Brent D. Bartlett and John R.  Schott},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Journal = {Journal of Applied Remote Sensing},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Keywords = {atmospheric inversion, empirical line method, ground truth, modtran, cloud cover, whole-sky image},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Month = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number = {1},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Pages = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Title = {Atmospheric compensation in the presence of clouds: an adaptive empirical line method (AELM) approach},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Url = {http://www.cis.rit.edu/DocumentLibrary/admin/uploads/CIS000046.pdf},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Volume = {3},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Year = {2009}}