A locally adaptable iterative RX detector

Authors:
Yuri P. Taitano;Brian A. Geier;Kenneth W. Bauer
Affiliations:
Air Force Institute of Technology, Wright Patterson AFB, OH;Air Force Institute of Technology, Wright Patterson AFB, OH;Air Force Institute of Technology, Wright Patterson AFB, OH
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Year:
2010

Citing 6
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Abstract

We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better first- and second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable Receiver Operating Characteristic (ROC) curves when compared to a global anomaly detector based upon the Support Vector Data Description (SVDD) algorithm, the conventional RX detector, and decomposed versions of the LAIRX detector. Furthermore, the utilization of parallel and distributed processing allows fast processing time making LAIRX applicable in an operational setting.