Automated material characterization and identification from airborne imagery is an important capability for many applications including geospatial database construction. Hyperspectral imagery provides a rich source of information for this purpose but utilization is complicated by the variability in a material's observed spectral signature due to the illumination and atmospheric conditions. In this paper, we present a method for the recovery of spectral reflectance using two views of a material in a scene acquired under unknown conditions. The method simultaneously allows recovery of spectral functions that can be used for atmospheric correction. The recovered reflectance can be used to build a hyperspectral subspace representation for a material that can be used for identification over a wide range of circumstances. We demonstrate the use of these algorithms for reflectance estimation and material mapping using HYDICE imagery acquired at Fort Hood, Texas. The method correctly maps several classes of roofing materials, asphalt roads, and vegetation over significant spectral changes due to variation in surface orientation. We show that the approach outperforms methods based on direct spectral comparison.