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.
We present an algorithm to estimate 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, orientation of the material surface,
and the atmospheric and illumination conditions determine the sensor radiance of a pixel. The algorithm
uses a low-dimensional coupled subspace model for the solar radiance, sky radiance, and path-scattered radiance.
The common inter-dependence of these spectra on the environmental condition and viewing geometry is considered
by using the coupled subspace model. The physics-based image formation model used by the algorithm
uses two orientation parameters which are used to determine the surface orientation. A constrained nonlinear
optimization method is used to estimate the orientation and the coupled-subspace model parameters. We have
tested the utility of our algorithm using a large set of 0.42-1.74 micron sensor radiance spectra simulated for
varying surface orientations of different materials.
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 non-linear algorithm to estimate the surface spectral reflectance given the sensor radiance spectrum corresponding to a single pixel. The algorithm uses a low-dimensional subspace model for the reflectance spectra. The solar radiance, sky radiance, and path-scattered radiance are dependent on the environmental condition and viewing geometry and this inter-dependence is considered by using a coupled subspace model for these spectra. The algorithm uses the Levenberg-Marquardt method to estimate the subspace model parameters which are used to determine the reflectance spectrum. We have applied the algorithm to a large set of 0.42-1.74 micron sensor radiance spectra simulated for different atmospheric conditions, materials, and surface orientations. We have also examined the utility of the algorithm for
reflectance recovery in digital imaging and remote sensing image generation (DIRSIG) scenes that contain 3D objects.
We present a nonlinear algorithm for estimating surface spectral reflectance from the spectral radiance measured by an airborne sensor. Estimation of surface reflectance is of importance since
it is independent of the atmospheric and illumination conditions.
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 simulated 0.4-1.74 micron sensor radiance spectra. A database of reflectance vectors and MODTRAN illumination, path radiance, and upward transmittance vectors for different atmospheric conditions were used to generate the sensor radiance spectra. We have examined the use of the recovered reflectance vectors for material identification over a database of materials.
We present a method for separating a sensor radiance spectrum into a reflectance spectrum and an illumination spectrum. The method is based on the use of subspace models for both reflectance and illumination spectra. The method exploits the fact that reflectance and illumination spectra typically lie in distinct subspaces. The separation algorithm finds the best reflectance and illumination spectra within their respective subspaces. We have applied the algorithm to simulated VNIR radiance spectra using a large database of reflectance and illumination spectra. We have also examined the use of the recovered reflectance spectra for material identification over a database of materials.