Modeling subpixel orientation effects for hyperspectral demixing

The radiance spectrum corresponding to a single pixel in an airborne or space-based hyperspectral image is dependent on the reflectance spectra and orientations of the material surfaces within the pixel area. We develop a hyperspectral demixing algorithm that estimates the pixel area fractions of multiple materials present within a pixel. The algorithm exploits a nonlinear physics-based image formation model that allows surfaces with multiple orientations within the pixel area. Geometric constraints are derived in conjunction with the image formation model. The algorithm involves solving a constrained nonlinear optimization problem to estimate the pixel area fractions and the surface orientation parameters. An experiment using simulated radiance spectra is presented to demonstrate the utility of the algorithm.