Recovery of Ground Surface Information from Airborne Hyperspectral Images

We present algorithms to estimate the surface spectral reflectance and the orientation of a ground material corresponding to a pixel in a hyperspectral image acquired by an airborne sensor under unknown atmospheric conditions. A physics-based image formation model is used in which the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions determine the sensor radiance of a pixel. The algorithm uses low-dimensional subspace models for the surface reflectance, solar radiance, sky radiance, and path-scattered radiance. The common inter-dependence of the illumination spectra and path- scattered radiance on the environmental condition and viewing geometry is considered by using a coupled subspace model. We use nonlinear optimization methods to estimate the subspace parameters and orientation parameters used in the physics-based image formation model. The subspace and orientation parameters are used for surface reflectance and orientation recovery. We have tested the utility of the algorithms using a large set of 0.42-1.74 micron sensor radiance spectra simulated for varying surface orientations of different materials.