Change detection using mean-shift and outlier-distance metrics

Joshua Zollweg; Ariel Schlamm; David B. Gillis; David Messinger

doi:10.1117/12.884503

20 May 2011 Change detection using mean-shift and outlier-distance metrics

Joshua Zollweg, Ariel Schlamm, David B. Gillis, David Messinger

Author Affiliations +

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 804808 (2011) https://doi.org/10.1117/12.884503
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Joshua Zollweg, Ariel Schlamm, David B. Gillis, and David Messinger "Change detection using mean-shift and outlier-distance metrics", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804808 (20 May 2011); https://doi.org/10.1117/12.884503

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