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Proceedings Paper

Image misregistration effects on hyperspectral change detection
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Paper Abstract

This paper covers the impact of registration errors between two images on chronochrome and covariance equalization predictors used for hyperspectral change detection. Hyperspectral change detection involves the comparison of data collected of the same spatial scene on two different occasions to try to identify anomalous man-made changes. Typical change detection techniques employ a linear prediction method followed by a subtraction step to identify changes. These linear predictors rely upon statistics from both scenes to determine a respective gain and offset. Chronochrome and covariance equalization remain two common predictors used in the change detection process. Chronochrome relies upon a cross-covariance matrix for prediction whereas covariance equalization relies solely upon the individual covariance matrices. In theory, chronochrome seems more susceptible to image misregistration issues as joint statistic estimates may suffer with registration error present. This paper examines the validity of this assumption. Using a push-broom style imaging spectrometer mounted on a pan and tilt, visible to near infrared data of scenes suitable for change detection analysis are gathered. The pan and tilt system ensures initial misregistration of the data is minimal. Using simple translations of the scenes, misregistration impacts upon prediction error and change detection are examined for varying degrees of shift.

Paper Details

Date Published: 2 May 2008
PDF: 10 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660Y (2 May 2008); doi: 10.1117/12.775435
Show Author Affiliations
Joseph Meola, Air Force Research Lab. (United States)
Michael T. Eismann, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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