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Anomaly Detection With Training Data In Hyperspectral Imagery

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IEEE ICASSP 2020 Virtual Conference May 2020

In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of fals

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Anomaly Detection With Training Data In Hyperspectral Imagery

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In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of fals