TY - GEN
T1 - Multiple-detector fusion for anomaly detection in multispectral imagery based on maximum entropy and nonparametric estimation
AU - Di, Wei
AU - Pan, Quan
AU - Zhao, Yong Qiang
AU - He, Lin
PY - 2006
Y1 - 2006
N2 - With the development of sensors capable of high spatial and spectral resolution, anomaly detection in Multispectral Imagery has gained more attention recently. However, using single detector meets great limitation due to that many of the conditions and parameters that govern performance are unknown or poorly characterized in an operational setting. Unlike the conventional fusion approaches simply using logical operators (e.g. AND, OR), which lead to produce highly variable performance results from one case and thus difficult to specify the "best" fusion logic in advance, a multiple-detector fusion (MDF) algorithm is proposed in this paper. Three successive procedures are included as follows: First, we use series anomaly detectors including well-known RX and its varieties to get the pilot detection results. Second, in order to estimate the pdf statistics of each individual detector's output more accurately, a nonparametric method called kernel density estimation (KDE) with bandwidth adjusted adoptively is used. The obtained probabilistic information are then fused using a modeled joint distribution by the principle of maximum entropy. Finally, the MDF approach is applied to real multispectral imagery. Experimental results and theoretical analysis demonstrate the effectiveness of proposed algorithm.
AB - With the development of sensors capable of high spatial and spectral resolution, anomaly detection in Multispectral Imagery has gained more attention recently. However, using single detector meets great limitation due to that many of the conditions and parameters that govern performance are unknown or poorly characterized in an operational setting. Unlike the conventional fusion approaches simply using logical operators (e.g. AND, OR), which lead to produce highly variable performance results from one case and thus difficult to specify the "best" fusion logic in advance, a multiple-detector fusion (MDF) algorithm is proposed in this paper. Three successive procedures are included as follows: First, we use series anomaly detectors including well-known RX and its varieties to get the pilot detection results. Second, in order to estimate the pdf statistics of each individual detector's output more accurately, a nonparametric method called kernel density estimation (KDE) with bandwidth adjusted adoptively is used. The obtained probabilistic information are then fused using a modeled joint distribution by the principle of maximum entropy. Finally, the MDF approach is applied to real multispectral imagery. Experimental results and theoretical analysis demonstrate the effectiveness of proposed algorithm.
KW - Anomaly detector
KW - Fusion
KW - Kernel density estimation (KDE)
KW - Maximum entropy (ME)
KW - Multispectral imagery
UR - http://www.scopus.com/inward/record.url?scp=34249319471&partnerID=8YFLogxK
U2 - 10.1109/ICOSP.2006.345778
DO - 10.1109/ICOSP.2006.345778
M3 - 会议稿件
AN - SCOPUS:34249319471
SN - 0780397371
SN - 9780780397378
T3 - International Conference on Signal Processing Proceedings, ICSP
BT - 8th International Conference on Signal Processing, ICSP 2006
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Signal Processing, ICSP 2006
Y2 - 16 November 2006 through 20 November 2006
ER -