Degree of familiarity ART2 in knowledge-based landmine detection

The self-organizing network ART2 is extended to provide a fuzzy output value, which indicates the degree of familiarity of a new analog 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 analog value to a fuzzy rule-based fusion technique which also uses a processed polarization 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 (to that of a previous network in [3]) has improved real-time landmine detection capabilities although the registration of all bands is more critical to the accuracy of results in this case. The real-time fuzzy rule-based system in preliminary tests has detected two of the three landmines and the landmine surrogate with two false alarms. Advanced tests on 30 images using the fuzzy rule-based system further confirmed the distinct advantages of fusion and improved detection rates.