Feature level fusion of polarimetric infrared and GPR data for landmine detection
conference paper
Feature-level sensor fusion is the process where specific information (i.e. features) from objects detected by different sensors are combined and classified. This paper focuses on the feature-level fusion procedure for a sensor combination consisting of a polarimetric infrared (IR) imaging sensor and a GPR: a video impulse radar (VIR). The single sensor detection methods and the feature-level sensor-fusion methods are evaluated. The detection results of both single sensors and the sensor-fusion methods are presented in receiver operator characteristics (ROC) curves. They show that on the training set feature-level sensor-fusion always outperforms the best single sensor. Furthermore, on the independent evaluation set there
are ROC points of the feature-level sensor-fusion methods that are better than the best sensor.
are ROC points of the feature-level sensor-fusion methods that are better than the best sensor.
TNO Identifier
95614
Publisher
Society for Counter Ordnance Technology (SCOT)
Source title
Proceedings EUDEM2-SCOT-2003. International conference on requirements and technologies for the detection, removal and neutralization of landmines and UXO, 15-18 Sep 2003, Vrije Universiteit Brussel, Brussles, Belgium. Volume 2
Editor(s)
Sahli, H.
Bottoms, A.M.
Cornelis, J.
Bottoms, A.M.
Cornelis, J.
Place of publication
Monterey, CA
Pages
638-642