Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions

The spectral radiance measured by an airborne imaging spectrometer for a ma terial on the Earth's surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limite d the success of material-identification algorithms that rely on hyperspect ral image data without associated ground-truth information. In this paper, we use a comprehensive physical model to show that the set of observed 0.4- 2.5 mu m spectral-radiance vectors for a material lies in a low-dimensional subspace of the hyperspectral-measurement space. The physical model captur es the dependence of the reflected sunlight, reflected skylight, and path-r adiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace mode l, we develop a local maximum-likelihood algorithm for automated material i dentification that is invariant to illumination, atmospheric conditions, an d the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. We show that the low dimensionality of mat erial subspaces allows for the robust discrimination of a large number of m aterials over a wide range of conditions. We demonstrate the invariant algo rithm for the automated identification of material samples in HYDICE imager y acquired under different illumination and atmospheric conditions.