20 October 1993 Neural network clutter-rejection model for FLIR ATR
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Abstract
Boundary shape is a significant and well-modeled property of forward-looking infrared (FLIR) sensor target signatures. State-of-the-art FLIR automatic target recognition (ATR) algorithms that rely on signature shape for target detection perform well but still fall short of human performance and many DoD requirements. We find that internal signature information significantly improves detection performance. The problem is that this information is not easily modeled, especially in FLIR signatures, because the signatures exhibit significant variations dependent on a large number of unknowns. We have developed a model-driven neural network technique, called Programmed Constructive Neural Networks (PCNN), that demonstrates superior performance and generalization compared to traditional back- propagation techniques in high noise applications. We have used the PCNN technique to model internal FLIR signature information for clutter rejection. Our PCNN FLIR clutter rejection model eliminates 75% of the false alarms in a state-of-the-art shape-based algorithm with minimal detection loss. This result was achieved even on scenarios not represented in the training set.
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Andrew P. Kramer, Andrew P. Kramer, Ajay Jain, Ajay Jain, } "Neural network clutter-rejection model for FLIR ATR", Proc. SPIE 1957, Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition, (20 October 1993); doi: 10.1117/12.161457; https://doi.org/10.1117/12.161457
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