TY - JOUR
T1 - Training data assisted anomaly detection of multi-pixel targets in hyperspectral imagery
AU - Liu, Jun
AU - Feng, Yutong
AU - Liu, Weijian
AU - Orlando, Danilo
AU - Li, Hongbin
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - In this paper, we investigate the anomaly detection problem for widespread targets with known spacial patterns under a local Gaussian model when training data are available. Three adaptive detectors are proposed based on the principles of the generalized likelihood ratio test, the Rao test, and the Wald test, respectively. We prove that these tests are statistically equivalent to each other. In addition, analytical expressions for the probability of false alarm and probability of detection of the proposed detectors are obtained, which are verified through Monte Carlo simulations. It is shown that these detectors have a constant false alarm rate against the covariance matrix. Finally, numerical examples using synthetic and real hyperspectral data demonstrate that these training data assisted detectors have better detection performance than their counterparts without training data.
AB - In this paper, we investigate the anomaly detection problem for widespread targets with known spacial patterns under a local Gaussian model when training data are available. Three adaptive detectors are proposed based on the principles of the generalized likelihood ratio test, the Rao test, and the Wald test, respectively. We prove that these tests are statistically equivalent to each other. In addition, analytical expressions for the probability of false alarm and probability of detection of the proposed detectors are obtained, which are verified through Monte Carlo simulations. It is shown that these detectors have a constant false alarm rate against the covariance matrix. Finally, numerical examples using synthetic and real hyperspectral data demonstrate that these training data assisted detectors have better detection performance than their counterparts without training data.
KW - Anomaly detection
KW - Rao test
KW - Wald test
KW - constant false alarm rate
KW - generalized likelihood ratio test
KW - hyperspectral images
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U2 - 10.1109/TSP.2020.2991311
DO - 10.1109/TSP.2020.2991311
M3 - Article
AN - SCOPUS:85086071100
SN - 1053-587X
VL - 68
SP - 3022
EP - 3032
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
M1 - 9082140
ER -