Irregular illumination across a hyperspectral image makes it difficult to detect targets in shadows, perform change detection, and segment the contents of the scene. To correct for the data in shadow, we first convert the data from Cartesian space to a hyperspherical coordinate system. Each N-dimensional spectral vector is converted to N-1 spectral angles and a magnitude representing the illumination value of the spectra. Similar materials will have similar angles and the differences in illumination will be described mostly by the magnitude. In the data analyzed, we found that the distribution of illumination values is well approximated by the sum of two- Gaussian distributions, one for shadow and one for non-shadow. The Levenberg-Marquardt algorithm is used to fit the empirical illumination distribution to the theoretical Gaussian sum. The LM algorithm is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued functions. Once the shadow and non-shadow distributions have been modeled, we find the optimal point to be one standard deviation out on the shadow distribution, allowing for the selection of about 84% of the shadows. This point is then used as a threshold to decide if the pixel is shadow or not. Corrections are made to the shadow regions and a spectral matched filter is applied to the image to test target detection in shadow regions. Results show a signal-to-noise gain over other illumination suppression techniques.© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.