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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.


Sourced from the publisher version at Springer Open:
Taitano, Y. P., Geier, B. A., & Bauer, K. W. (2010). A Locally Adaptable Iterative RX Detector. EURASIP Journal on Advances in Signal Processing, 2010(1), 341908.

Copyright © 2010 Yuri P. Taitano et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



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EURASIP Journal on Advances in Signal Processing