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20 May 2011 Extension and implementation of a model-based approach to hyperspectral change detection
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Abstract
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.
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Joseph Meola, Michael T. Eismann, Randolph L. Moses, and Joshua N. Ash "Extension and implementation of a model-based approach to hyperspectral change detection", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804806 (20 May 2011); https://doi.org/10.1117/12.883265
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