Extension and implementation of a model-based approach to hyperspectral change detection

摘要

A new method for hyperspectral change detection derived from a parametric radiative transfer model was recently developed. The model-based approach explicitly accounts for local illumination variations, such as shadows, which act as a constant source of false alarms in traditional change detection techniques. Here we formally derive the model-based approach as a generalized likelihood ratio test (GLRT) developed from the data model. Additionally, we discuss variations on implementation techniques for the algorithm and provide results using tower-based data and HYDICE data.