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Proceedings Paper

Information theory for the prediction of SAR target classification performance
Author(s): Andrew M. Horne
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Paper 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.

Paper Details

Date Published: 27 August 2001
PDF: 12 pages
Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438235
Show Author Affiliations
Andrew M. Horne, Defence Evaluation and Research Agency Malvern (United Kingdom)

Published in SPIE Proceedings Vol. 4382:
Algorithms for Synthetic Aperture Radar Imagery VIII
Edmund G. Zelnio, Editor(s)

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