Invariant subpixel material detection in hyperspectral imagery

We present an algorithm for subpixel material detection in hyperspectral data that is invariant to the illumination and atmospheric conditions. The algorithm does not require atmospheric correction. The target material spectral reflectance is the only required prior information. A target material subspace model is constructed from the reflectance using a physical model and a background subspace model is estimated directly from the image. These two subspace models are used to compute maximum-likelihood estimates (MLEs) for the target material component and the background component at each image pixel. These estimates form the basis of a generalized likelihood ratio test for subpixel material detection. We present experimental results, using Hyperspectral Digital Imagery Collection Experiment (HYDICE) imagery, that demonstrate the utility of the algorithm for subpixel material detection under varying illumination and atmospheric conditions.