Paper
2 February 2009 Uncorrelated versus independent elliptically-contoured distributions for anomalous change detection in hyperspectral imagery
James Theiler, Clint Scovel
Author Affiliations +
Proceedings Volume 7246, Computational Imaging VII; 72460T (2009) https://doi.org/10.1117/12.814325
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive differences that inevitably arise whenever two pictures are taken of the same scene, but at different times and under different conditions. These differences include effects due to illumination, calibration, misregistration, etc. If the actual changes are assumed to be rare, then one can "learn" what the pervasive differences are, and can identify the deviations from this pattern as the anomalous changes. A recently proposed framework for anomalous change detection recasts the problem as one of binary classification between pixel pairs in the data and pixel pairs that are independently chosen from the two images. When an elliptically-contoured (EC) distribution is assumed for the data, then analytical expressions can be derived for the measure of anomalousness of change. However, these expression are only available for a limited class of EC distributions. By replacing independent pixel pairs with uncorrelated pixel pairs, an approximate solution can be found for a much broader class of EC distributions. The performance of this approximation is investigated analytically and empirically, and includes experiments comparing the detection of real changes in real data.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Theiler and Clint Scovel "Uncorrelated versus independent elliptically-contoured distributions for anomalous change detection in hyperspectral imagery", Proc. SPIE 7246, Computational Imaging VII, 72460T (2 February 2009); https://doi.org/10.1117/12.814325
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Cited by 5 scholarly publications.
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KEYWORDS
Sensors

Hyperspectral imaging

Signal detection

Binary data

Data modeling

Calibration

Detection and tracking algorithms

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