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Subpixel Anomalous Change Detection in Remote Sensing Imagery

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1 Author(s)
Theiler, J. ; Space & Remote Sensing Sci., Los Alamos Nat. Lab., Los Alamos, NM

A machine-learning framework for anomalous change detection is extended to the situation in which the anomalous change is smaller than a pixel. Although the existing framework can be applied to (and does have power against) the subpixel case, it is possible to optimize that framework for the subpixel case when the size of the anomalous change is known. The limit of intesimally small anomaly turns out to be well- defined, and provides a new parameter-free anomalous change detector which is effective over a range of subpixel anomalies, and continues to have reasonable power against the full-pixel case.

Published in:

Image Analysis and Interpretation, 2008. SSIAI 2008. IEEE Southwest Symposium on

Date of Conference:

24-26 March 2008