Anomaly Detection with Training Data in Hyperspectral Imagery

Jun Liu; Yutong Feng; Weijian Liu; Danilo Orlando; Hongbin Li

ICASSP 2020accepted0 citations

Anomaly Detection with Training Data in Hyperspectral Imagery

Jun Liu, Yutong Feng, Weijian Liu, Danilo Orlando, Hongbin Li

Abstract

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.

BibTeX

@inproceedings{icassp2020_anomalydetection,
  title = {Anomaly Detection with Training Data in Hyperspectral Imagery},
  author = {Jun Liu and Yutong Feng and Weijian Liu and Danilo Orlando and Hongbin Li},
  booktitle = {ICASSP 2020},
  year = {2020}
}