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Proceedings Paper

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

Paper Details

Date Published: 20 May 2011
PDF: 13 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804806 (20 May 2011); doi: 10.1117/12.883265
Show Author Affiliations
Joseph Meola, U.S. Air Force Research Lab. (United States)
Michael T. Eismann, U.S. Air Force Research Lab. (United States)
Randolph L. Moses, The Ohio State Univ. (United States)
Joshua N. Ash, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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