A Locally Adaptable Iterative RX Detector. (10th May 2010)

• Record Type: Journal Article
• Authors: Taitano Taitano, Yuri P. Yuri P.; Geier Geier, Brian A. Brian A.; Bauer Bauer, Kenneth W. Kenneth W.
• Other Names: Du Du Yingzi Yingzi Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-05-10
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/341908
• Languages: English
• ISSNs: 1687-6172

Abstract

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.