22 June 1994 Comparison of polynomial network and model-based target recognition
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Abstract
Model-based and data-driven approaches to automatic target recognition each provide a methodology to determine the class of an unknown target. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of unknown targets. A comparator algorithm determines a degree of `match' to each reference target. Data-driven approaches use a numeric algorithm to process a set of characterization features to produce a class likelihood estimate. Each approach has advantages and limitations that should be considered for a specific implementation. This research compares a specific implementation of each of these approaches developed for an automatic target recognition system that processes multi- spectral imagery representing military targets. To provide a valid baseline to compare the performance of each approach, a common target set, characterization feature set, and performance metrics are considered.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith C. Drake, Richard Y. Kim, Tony Y. Kim, Owen D. Johnson, "Comparison of polynomial network and model-based target recognition", Proc. SPIE 2233, Sensor Fusion and Aerospace Applications II, (22 June 1994); doi: 10.1117/12.179030; https://doi.org/10.1117/12.179030
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