Information theory for the prediction of SAR target classification performance

Andrew M. Horne

doi:10.1117/12.438235

27 August 2001 Information theory for the prediction of SAR target classification performance

Andrew M. Horne

Proceedings Volume 4382, Algorithms for Synthetic Aperture Radar Imagery VIII; (2001) https://doi.org/10.1117/12.438235
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

This paper develops a high level theoretical framework describing quantitatively the potential ability of a synthetic aperture or similar imaging radar to classify discrete military targets. Communications information theory is used to calculate the information conveyed by the image of a target from the values of image pixels relative to the non-deterministic fluctuations of those values. The classification problem being addressed is scoped by defining a set of target classes and calculating the degree of deterministic variability present within each class. The probability of correct classification is determined by setting the information conveyed by the image against the scope of the classification problem to be solved. The theory is validated against simulated target classification experiments. It is then shown how the theory may be applied at a detailed level to a specific target classification algorithm, and at a high level in algorithm-independent performance prediction.

Citation Download Citation

Andrew M. Horne "Information theory for the prediction of SAR target classification performance", Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); https://doi.org/10.1117/12.438235

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