Using a linear subspace approach for invariant subpixel material identification in airborne hyperspectral imagery

We present an algorithm for subpixel material identification that is invariant to the illumination and atmospheric conditions. The target material spectral reflectance is the only prior information required by the algorithm. 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 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 identification. We present experimental results using HYDICE imagery that demonstrate the utility of the algorithm for subpixel material identification under varying illumination and atmospheric conditions.