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- W2000361322 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." @default.
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- W2000361322 date "2010-05-10" @default.
- W2000361322 modified "2023-10-14" @default.
- W2000361322 title "A Locally Adaptable Iterative RX Detector" @default.
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- W2000361322 doi "https://doi.org/10.1155/2010/341908" @default.
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