A Locally Adaptable Iterative RX Detector
Document Type
Article
Publication Date
5-10-2010
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.
DOI
10.1155/2010/341908
Source Publication
EURASIP Journal on Advances in Signal Processing
Recommended Citation
Taitano, Y.P., Geier, B.A. & Bauer, K.W. A Locally Adaptable Iterative RX Detector. EURASIP J. Adv. Signal Process. 2010, 341908 (2010). https://doi.org/10.1155/2010/341908.
Comments
The "Link to Full Text" on this page loads the open access article hosted at the SpringerOpen portal.
This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 2.0)