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Proceedings Paper

Change detection using mean-shift and outlier-distance metrics
Author(s): Joshua Zollweg; Ariel Schlamm; David B. Gillis; David Messinger
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Paper Abstract

Change detection with application to wide-area search seeks to identify where interesting activity has occurred between two images. Since there are many different classes of change, one metric may miss a particular type of change. Therefore, it is potentially beneficial to select metrics with complementary properties. With this idea in mind, a new change detection scheme was created using mean-shift and outlier-distance metrics. Using these metrics in combination should identify and characterize change more completely than either individually. An algorithm using both metrics was developed and tested using registered sets of multispectral imagery.

Paper Details

Date Published: 20 May 2011
PDF: 10 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804808 (20 May 2011); doi: 10.1117/12.884503
Show Author Affiliations
Joshua Zollweg, Rochester Institute of Technology (United States)
Ariel Schlamm, Rochester Institute of Technology (United States)
David B. Gillis, U.S. Naval Research Lab. (United States)
David Messinger, Rochester Institute of Technology (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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