Online Active Learning for Automatic Target Recognition

Automatic target recognition in sidescan sonar imagery is vital to many applications, particularly sea mine detection and classification. We expand upon the traditional offline supervised classification approach with an active learning method to automatically label new objects that are not present in the training set. This is facilitated by the option of sending difficult samples to an outlier bin, from which models can be built for new objects. The decisions of the classifier are improved by a novel active learning approach, called model trees (MT), which builds an ensemble of hypotheses about the classification decisions that grows proportionally to the amount of uncertainty the system has about the samples. Our system outperforms standard active learning methods, and is shown to correctly identify new objects much more accurately than a pure clustering approach, on a simulated sidescan sonar data set.