Grouping multiple neural networks for automatic target recognition in infrared imagery

Aiming at the automatic recognition of motorized vehicles in cluttered infrared images. this paper presents an approach for grouping multiple supervised neural networks into an efficient classifier. taking into account the variety of targets and background scenarios available in the imagery provided for the EUCLID project RTP 8.2. The proposed neural network architecture consists of a modular combination of several small multi-layer-perceptron neural networks. To take into account the false targets generated by the detection stage. along with the networks used for the discrimination between a target class and all the other classes of targets. auxiliary neural networks aim to separate targets from non-targets. For ambiguous situations it is also introduced an additional level of neural networks trained to discriminate sub-groups of classes that present similar features. Training and testing leas performed using five classes of targets within cluttered environments: tanks. trucks. cars. airplanes and helicopters. Most of the data used was from real infrared imagery, although complementary synthetic target models were also introduced to test the validity of the presented approach in a wide variety of situations.