TY - GEN
T1 - Considering spatial information to improve anomaly detection in heterogeneous hyperspectral images
AU - Weber, F.
AU - Lefebvre, S.
AU - Moulines, E.
AU - Bousquet, M.
AU - Roux, N.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/28
Y1 - 2016/6/28
N2 - The aim of this paper is to assess the gain of accuracy obtained by taking into account spatial information for anomaly detection in hyperspectral imaging. A mixture of conditional vector autoregressive model, MixCVAR, is introduced for background pixels. It is exploited to construct an anomaly detector (AD) based on generalized likelihood ratio test (GLRT). In the considered detection task, this detector outperforms the SEM-RX detector [1].
AB - The aim of this paper is to assess the gain of accuracy obtained by taking into account spatial information for anomaly detection in hyperspectral imaging. A mixture of conditional vector autoregressive model, MixCVAR, is introduced for background pixels. It is exploited to construct an anomaly detector (AD) based on generalized likelihood ratio test (GLRT). In the considered detection task, this detector outperforms the SEM-RX detector [1].
KW - Anomaly detection
KW - Heterogeneous texture
KW - Hyperspectral
UR - https://www.scopus.com/pages/publications/85037525603
U2 - 10.1109/WHISPERS.2016.8071733
DO - 10.1109/WHISPERS.2016.8071733
M3 - Conference contribution
AN - SCOPUS:85037525603
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2016 8th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Y2 - 21 August 2016 through 24 August 2016
ER -