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Proceedings Paper

A comparative study of four change detection methods for aerial photography applications
Author(s): Gil Abramovich; Glen Brooksby; Stephen F. Bush; Swaminathan Manickam; Ozge Ozcanli; Benjamin D. Garrett
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Paper 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.

Paper Details

Date Published: 26 April 2010
PDF: 12 pages
Proc. SPIE 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII, 76680M (26 April 2010); doi: 10.1117/12.852195
Show Author Affiliations
Gil Abramovich, GE Global Research (United States)
Glen Brooksby, GE Global Research (United States)
Stephen F. Bush, GE Global Research (United States)
Swaminathan Manickam, GE Global Research (United States)
Ozge Ozcanli, Brown Univ. (United States)
Benjamin D. Garrett, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 7668:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII
Daniel J. Henry, Editor(s)

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