Using coupled subspace models for recovery of reflectance spectra from airborne images

The spectral radiance measured by an airborne sensor is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present a nonlinear algorithm for estimating surface spectral reflectance of a surface on the ground from the spectral radiance measured by an airborne sensor. The nonlinear separation algorithm uses a low-dimensional subspace model for the reflectance spectra. The algorithm also considers the inter-dependence of the path radiance and illumination spectra by using a coupled subspace model. We have applied the algorithm to a large set of 0.4-1.74 micron sensor radiance spectra. We have examined the use of the recovered reflectance vectors for material identification over a database of materials from the US geological Survey (USGS) library. We also extend the nonlinear algorithm to estimate the reflectance spectra of materials having varying orientations. We have applied the algorithm to 0.42-1.74 micron sensor radiance spectra in digital imaging and remote sensing image generation (DIRSIG) model scenes that contain 3D objects.