Adaptive Infrared Target Detection

McBride, J., Stevens, M. R., Eaton, R., and Snorrason, M.

Proceedings SPIE Defense & Security, vol 5426, Orlando, FL (April 2004)

Automatic Target Recognition (ATR) algorithms are extremely sensitive to differences between the operating conditions under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. These extended operating conditions can cause a target’s signature to be drastically different from training exemplars/models. For example, a target’s signature can be influenced by the following: time of day, time of year, weather, atmospheric conditions, position of the sun or other illumination sources, target surface and material properties, target composition, target geometry, sensor characteristics, sensor viewing angle and range, target surroundings and environment, and target and scene temperature. Recognition rates degrade if an ATR is not trained for a particular EOC. Most infrared target detection techniques are based on a very simple probabilistic theory. This theory states that a pixel should be assigned the label of “target” if a set of measurements (features) is more likely to have come from an assumed (or learned) distribution of target features than from the distribution of background features. However, most detection systems treat these learned distributions as static and they are not adapted to changing EOCs. In this paper, we present an algorithm for assigning a pixel the label of target or background based on a statistical comparison of the distributions of measurements surrounding that pixel in the image. This method provides a feature-level adaptation to changing EOCs. Results are demonstrated on infrared imagery containing several military vehicles.

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