TY - GEN
T1 - Anomaly Detection with Training Data in Hyperspectral Imagery
AU - Liu, Jun
AU - Feng, Yutong
AU - Liu, Weijian
AU - Orlando, Danilo
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - generalized likelihood ratio test
KW - hyperspectral imagery
KW - training data
UR - http://www.scopus.com/inward/record.url?scp=85089228307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089228307&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9053848
DO - 10.1109/ICASSP40776.2020.9053848
M3 - Conference contribution
AN - SCOPUS:85089228307
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4836
EP - 4840
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -