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)

A. G. Yarovoy

F. Cremer

K. Schutte

R. F. Bloemenkamp

V. Kovalenko

W. De Jong

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

Cremer, F.

Jong, W. de

Schutte, K.

Yarovoy, A.G.

Kovalenko, V.

Bloemenkamp, R.F.

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Feature level fusion of polarimetric infrared and GPR data for landmine detection

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6223

Feature level fusion of polarimetric infrared and GPR data for landmine detection

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