In this paper the multi sensor fusion results obtained within the European research project GEODE (Ground Explosive Ordnance Detection system) are presented. The lay out of the test lane and the individual sensors used are described. The implementation of the SCooP algorithrn improves the RoC curves, as the false alarm surface and the number of false alarms b or manufacturers, of the sensors are used as input fusion methods implemented are Bayes, Dempste grids to the input par¿rmeters for fusion methods entire test lane is used for training and evaluation. All four sensor fusion methods provide better detection results than the individual sensors.
This paper gives a comparison of two vehicle-mounted infrared systems for landmine detection. The first system is a downward looking standard infrared camera using processing methods developed within the EU project LOTUS. The second system is using a forward-looking polarimetric infrared camera. Feature-based classification is used for this system. With these systems data have been acquired simultaneously of different test lanes from a moving platform. The performance of each system is evaluated using a leave-one-out method. On the training set the polarimetric infrared system performs better especially for low false alarm rates. On the independent evaluation set the differences are much smaller. On the ferruginous soil test lane the down-ward looking system performs better at certain points whereas on the grass test lane the forward-looking system performs better at certain points.
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