Anomaly Detection with Training Data in Hyperspectral Imagery

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 false alarm and probability of detection. The performance of the proposed detector is evaluated by using simulated and real data. The results demonstrate that this training data assisted detector outperforms its counterpart without training data.

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