Invariant Subpixel Material Identification in Aviris Imagery

We present an algorithm for subpixel material identi cation that is invariant to the illumination and atmospheric conditions. The target material spectral re ectance is the only prior information required by the algorithm. A target material subspace model is constructed from the re ectance using an image formation 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 identi cation. We present experimental results using AVIRIS imagery that demonstrate the utility of the algorithm for subpixel material identi cation under varying illumination and atmospheric conditions.