Proceedings of SPIE Defense & Security, Automatic Target Recognition XVIII, Orlando, FL (2008)
Automatic target detection (ATD) systems process imagery to detect and locate targets in support of intelligence, surveillance, reconnaissance, and strike missions. Accurate prediction of ATD performance would assist in system design and trade studies, collection management, and mission planning. Specifically, a need exists for ATD performance prediction based exclusively on information available from the imagery and its associated metadata. In response to this need, we undertake a modeling effort that 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 ATD algorithms, which is even applicable when no image truth is available (e.g., when evaluating denied area imagery). Ongoing efforts to develop such a prediction model have met with some success. Previous results presented models to predict performance for several ATD methods. This paper extends the work in several ways: extension to a new ATD method, application of the modeling to a new image set, and an investigation of systematic changes in the image properties (resolution, noise, contrast). The paper concludes with a discussion of future research.
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