Abstract
After the most prominent signal in an infrared image of the sky is extracted, the question is whether the signal corresponds to an aircraft. We present a new approach that avoids metric similarity measures and the use of thresholds, and instead attempts to learn similarity measures like those used by humans. In the absence of sufficient real data, the approach allows one to specifically generate an arbitrarily large number of training exemplars projecting near the classification boundary. Once trained on such a training set, the performance of our neural network-based system is comparable to that of a human expert and far better than a network trained only on the available real data. Furthermore, the results obtained are considerably better than those obtained using an Euclidean discriminator.
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