Proceedings of SPIE Defense & Security, vol. 6566, Orlando, FL (May, 2007).
Automatic target detection (ATD) systems process imagery to detect and locate targets in imagery in support of a variety of military missions. Accurate prediction of ATD performance would assist in system design and trade stud-ies, collection management, and mission planning. A need exists for ATD performance prediction based exclusively on information available from the imagery and its associated metadata. We present a predictor based on image measures quantifying the intrinsic ATD difficulty on an image. The modeling effort consists of two phases: a learning phase, where image measures are computed for a set of test images, the ATD performance is measured, and a prediction model is developed; and a second phase to test and validate performance prediction. The learning phase produces a mapping, valid across various ATR algorithms, which is even applicable when no image truth is avail-able (e.g., when evaluating denied area imagery). The testbed has plug-in capability to allow rapid evaluation of new ATR algorithms. The image measures employed in the model include: statistics derived from a constant false alarm rate (CFAR) processor, the Power Spectrum Signature, and others. We present performance predictors for two trained ATD classifiers, one constructed using using GENIE Pro™, a tool developed at Los Alamos National Laboratory, and the other eCognition™, developed by Definiens. We present analyses of the two performance predictions, and compare the underlying prediction models. The paper concludes with a discussion of future research.
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