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Accurate target detection and classification of hyperspectral imagery require that the measurement by the imager matches as closely as possible the known “true” target as collected under controlled conditions and stored in a classification database. Therefore, the effect of the radiation source and the atmosphere must be factored out of the result before detection is attempted. Our objective is to investigate the uncertainty in the detections due to the uncertainty in the estimation of atmospherics. We apply a range of atmospheric profiles, correlated with relative humidity, to a MODTRAN-based prediction of the radiative transfer effect on simulated imagery using the Digital Imaging and Remote Sensing Image Generation Model. These profiles are taken from known distribution percentiles as obtained from historic meteorological measurements at the simulated site. We demonstrate the detection error, as measured by the Bhattacharyya coefficient, given the range of atmospheric conditions in the historic profile, and show that changes in the atmospheric assumptions change the values of the output for the adaptive matched filter.