The majority of pixel-level hyperspectral change detection algorithms have risen out of probabilistic models
developed for the data. These algorithms typically operate in two stages. In the first stage, the illumination
differences and other changes due to atmospheric and environmental conditions between the two scenes are
removed. In the second stage, a hypothesis test is performed on the difference between these normalized pixels.
These particular change detection methods often suffer due to local variability within the data. As an alternative
to these statistical-based change detection algorithms, this paper examines the use of a parametric physical model
towards change detection. For a single hyperspectral data set, the number of unknown parameters in the model
is greater than the number of measurements. However, if a second data set exists and the underlying material reflectance of each pixel is assumed to remain constant between the two, one can develop a problem for which the number of measurements is greater than the number of unknowns allowing for application of standard constrained optimization methods for parameter estimation. Assuming the validity of the physical model used, any residual error remaining after obtaining the optimal parameter estimates must result from noise or a violation of the reflectance assumption made, i.e., a change in material reflectance from time-1 to time-2. Accordingly, the fit error for each pixel is an indicator of reflectance change. Additionally, the proposed framework allows for incorporating spatial information at some later point. This paper provides a preliminary look at the proposed change detection method and associated challenges.