A Locally Adaptable Iterative RX Detector

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-...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2010-01, Vol.2010 (1), Article 341908
Main Authors: Taitano, Yuri P., Geier, Brian A., Bauer, Kenneth W.
Format: Article
Language:English
Subjects:
Advanced Image Processing for Defense and Security Applications
Algorithms
Anomalies
Avoidance
Detectors
Engineering
Mathematical analysis
Quantum Information Technology
Receivers
Research Article
Signal,Image and Speech Processing
Spintronics
Statistics
Vectors (mathematics)
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Summary: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.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1155/2010/341908