An Image Metric-Based ATR Performance Prediction Testbed

Ralph, S., Irvine, J., Stevens, M. R., and Vanstone, D.

Applied Imagery Pattern Recognition (AIPR) annual workshop, Washington, DC (October, 2005)

Currently, automatic target recognition (ATR) evaluation techniques use simple models, such as quick-look models, or detailed exhaustive simulation. Simple models cannot accurately quantify performance, while the detailed simulation requires enumerating each operating condition. A need exists for ATR performance prediction based on more accurate models. We develop a predictor based on image measures quantifying the intrinsic ATR difficulty on an image. These measures include: CFAR, Power Spectrum Signature, Probability of edge, etc.

We propose a two-phase approach: a learning phase, where image measures are computed on set of test images, and the ATR performance measured; and a performance prediction phase. The learning phase produces a mapping, valid across various ATR algorithms, even applicable when no image truth is available (e.g. evaluation for denied area imagery).

We present a performance predictor using a trained classifier ATR constructed using GENIE, a tool from Los Alamos.

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