26 April 2010 A comparative study of four change detection methods for aerial photography applications
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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


Towards a parameterless 3D mesh segmentation
Proceedings of SPIE (March 20 2013)
Measuring marine oil spill extent by Markov Random Fields
Proceedings of SPIE (October 14 2014)
AERICOMP: an aerial photo comparison system
Proceedings of SPIE (August 04 2000)
Automatic identification of vehicle license plates
Proceedings of SPIE (September 24 2007)

Back to Top