We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR)
sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the
number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory
is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief
assignment is used to transform this map into a belief map. The detection results of sensors are combined to
build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision
level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database
generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion
scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.
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