Automatic Aircraft Recognition: Toward Using Human Similarity Measure in a Recognition System

Abstract

The problem of screening images of the skies to determine whether they contain aircraft or not is both of theoretical and practical interest. After the most prominent visual signal in the infrared image of the sky is extracted, the question is whether the signal is a correct match of an aircraft. Common approaches calculate the degree of similarity of the shape of the signal with a model aircraft using a similarity measure such as Euclidean distance, and make a decision based on whether the degree of similarity exceeds a (pre-specified) threshold. Our approach avoids metric similarity measures and the use of thresholds as it attempts to employ similarity measures used by humans. In the absence of sufficient real data, the approach allows to specifically generate an arbitrarily large number of training exemplars projecting near classification boundary. Once trained on such a training set, the performance of the neural network was comparable to that of a human expert, and far better than a network trained only on the available real data. Furthermore, the results were considerably better than those obtained using a Euclidean discriminator.