Hyperspectral airborne sensing systems frequently employ spectral signature databases to detect materials. To achieve high detection and low false alarm rates, it is critical to retrieve accurate reflectance values from the camera’s digital number (dn) output. A one-time camera calibration converts dn values to reflectance. However, changes in solar angle and atmospheric conditions distort the reflected energy, reducing detection performance of the system.<p> </p>Changes in solar angle and atmospheric conditions introduce both additive (offset) and multiplicative (gain) effects for each waveband. A gain and offset correction can mitigate these effects. Correction methods based on radiative transfer models require equipment to measure solar angle and atmospheric conditions. Other methods use known reference materials in the scene to calculate the correction, but require an operator to identify the location of these materials. Our unmanned airborne vehicles application can use no additional equipment or require operator intervention. Applicable automated correction approaches typically analyze gross scene statistics to find the gain and offset values. Airborne hyperspectral systems have high ground resolution but limited fields-of-view, so an individual frame does not include all the variation necessary to accurately calculate global statistics.<p> </p>In the present work we present our novel approach to the automatic estimation of atmospheric and solar effects from the hyperspectral data. Our approach is based on Hough transform matching of background spectral signatures with materials extracted from the scene. Scene materials are identified with low complexity agglomerative clustering. Detection results with data gathered from recent field tests are shown.