2018
DOI: 10.1109/tgrs.2018.2810124
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BASO: A Background-Anomaly Component Projection and Separation Optimized Filter for Anomaly Detection in Hyperspectral Images

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Cited by 54 publications
(22 citation statements)
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“…Interestingly, if we compare (17) to (15) we immediately discover that R-AD and CEM share similar function forms with the desired target signature d used in (15) replaced by the data sample r subject to a normalization constant ( )…”
Section: Anomaly Detectionmentioning
confidence: 99%
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“…Interestingly, if we compare (17) to (15) we immediately discover that R-AD and CEM share similar function forms with the desired target signature d used in (15) replaced by the data sample r subject to a normalization constant ( )…”
Section: Anomaly Detectionmentioning
confidence: 99%
“…It is worth noting that there is no need of performing DS on the data space for R-AD. This is because inverting the correlation matrix R in (17) to suppress BKG has a similar effect as DS performed on the data space to remove the second order data statistics. It also indicates that there is no need of DS implemented in R-AD.…”
Section: A Hydice 15-panel Scenementioning
confidence: 99%
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“…The concept of anomaly detection has been broadly explored in different research domains such as computer network, image recognition, and machine operation [19]- [23]. In the context of power systems, this concept has been generally studied in the main grid sectors.…”
Section: Introductionmentioning
confidence: 99%
“…With the assumption of similarity of spectral features and spatial features of the background, many of the latest anomaly detection (AD) attempt to learn a dictionary and detected anomaly by reconstruction error [23]- [27]. Other methods apply slowly varying signal analysis [28], matrix decomposition [29], [30] or optimal filters [31], [32] to detected targets.…”
Section: Introductionmentioning
confidence: 99%