Abstract
The Adaptive Resonance Theory 2 (ART2) neural network architecture is extended to provide a fuzzy output value, which indicates the degree of familiarity of a new analogue input pattern to previously stored patterns in the long term memory of the network. The outputs of the multilayer perceptron and this modified ART2 provide an analogue value to a fuzzy rule-based fusion technique which also uses a processed polarisation resolved image as its third input. In real-time situations these two classifier outputs indicate the likelihood of a surface landmine target when presented with a number of multispectral and textural bands. Due to the modifications in ART2, this updated alternative architecture has improved real-time landmine detection capabilities.
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Filippidis, A., Russo, M., Jain, L.C. (2000). Novel Extension of ART2 in Surface Landmine Detection. In: Jain, L.C., Lazzerini, B., Halici, U. (eds) Innovations in ART Neural Networks. Studies in Fuzziness and Soft Computing, vol 43. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1857-4_1
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DOI: https://doi.org/10.1007/978-3-7908-1857-4_1
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