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Proceedings Paper

ROC analysis of ATR from SAR images using a model-based recognizer incorporating pose information
Author(s): David Cyganski; Brian M. King; Richard F. Vaz; John A. Orr
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Paper Abstract

An automatic taget recognition (ATR) technique developed by the authors features analytically derived object models which are formed from entire image suites, yet are compact and allow a direct target recognition and pose determination procedure. In contrast to the pose-invariant information used to form the models in conventional approaches, view-dependent information is retained in the formation of the compact models for this new approach. All model-based ATR systems are confronted with the problem of image variation as a function of viewing angle. This problem can be addressed by use of an exhaustive library of views, at the expense of a large suite of literal images and a computationally intensive search-based recognition process. Means for overcoming these storage and processing obstacles have traditionally invloved some type of view-independent target representation, often developed from some composite view of the target over the viewing angles of interest. This results in a much more compact target model, and a more direct recognition process. Unfortunately, the gains in storage and computational requirements of these invariant algorithms come at the price of diminished target discrimination capability. The new algorithm incorporates pose as a fundamental parameter which is solved for as part of the recognition process, and does not discard the pose-related information which is relevant to target recognition. In this paper, the newly developed technique is applied to synthetic aperture radar images to develop receiver operating characteristic curves in the presence of both multiplicative noise and clutter. Comparative curves are also developed for a conventional generalized quandratic classifier ATR system.

Paper Details

Date Published: 5 June 1995
PDF: 11 pages
Proc. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, (5 June 1995); doi: 10.1117/12.210838
Show Author Affiliations
David Cyganski, Worcester Polytechnic Institute (United States)
Brian M. King, Worcester Polytechnic Institute (United States)
Richard F. Vaz, Worcester Polytechnic Institute (United States)
John A. Orr, Worcester Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 2487:
Algorithms for Synthetic Aperture Radar Imagery II
Dominick A. Giglio, Editor(s)

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