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Proceedings Article

Resampling approach for anomalous change detection

[+] Author Affiliations
James Theiler

Los Alamos National Lab.

Simon Perkins

UltraSpectral, Inc.

Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651U (May 07, 2007); doi:10.1117/12.719972
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From Conference Volume 6565

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, Florida, USA | April 09, 2007

abstract

We investigate the problem of identifying pixels in pairs of co-registered images that correspond to real changes on the ground. Changes that are due to environmental differences (illumination, atmospheric distortion, etc.) or sensor differences (focus, contrast, etc.) will be widespread throughout the image, and the aim is to avoid these changes in favor of changes that occur in only one or a few pixels. Formal outlier detection schemes (such as the one-class support vector machine) can identify rare occurrences, but will be confounded by pixels that are "equally rare" in both images: they may be anomalous, but they are not changes. We describe a resampling scheme we have developed that formally addresses both of these issues, and reduces the problem to a binary classification, a problem for which a large variety of machine learning tools have been developed. In principle, the effects of misregistration will manifest themselves as pervasive changes, and our method will be robust against them - but in practice, misregistration remains a serious issue.

© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

James Theiler and Simon Perkins
"Resampling approach for anomalous change detection", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651U (May 07, 2007); doi:10.1117/12.719972; http://dx.doi.org/10.1117/12.719972


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