26 April 2010 A comparative study of four change detection methods for aerial photography applications
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Abstract
We present four new change detection methods that create an automated change map from a probability map. In this case, the probability map was derived from a 3D model. The primary application of interest is aerial photographic applications, where the appearance, disappearance or change in position of small objects of a selectable class (e.g., cars) must be detected at a high success rate in spite of variations in magnification, lighting and background across the image. The methods rely on an earlier derivation of a probability map. We describe the theory of the four methods, namely Bernoulli variables, Markov Random Fields, connected change, and relaxation-based segmentation, evaluate and compare their performance experimentally on a set probability maps derived from aerial photographs.
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Gil Abramovich, Glen Brooksby, Stephen F. Bush, Swaminathan Manickam, Ozge Ozcanli, Benjamin D. Garrett, "A comparative study of four change detection methods for aerial photography applications", Proc. SPIE 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII, 76680M (26 April 2010); doi: 10.1117/12.852195; https://doi.org/10.1117/12.852195
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