Invariant recognition in hyperspectral images

The spectral radiance measured for a material by an airborne hyperspectral sensor depends strongly on. The illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely exclusively on the information contained in hyperspectral image data. In this paper we use a comprehensive physical model to show that the set of observed 0.4-2.5 μm spectral radiance vectors for a material lies in a lour-dimensional subspace of the hyperspectral measurement space. The physical model captures the dependence of reflected sunlight, reflected skylight, and path radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximum likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions