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Proceedings Paper

Analysis of multiple-view Bayesian classification for SAR ATR
Author(s): Myron Z. Brown
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Paper Abstract

Classification of targets in high-resolution synthetic aperture radar imagery is a challenging problem in practice, due to extended operating conditions such as obscuration, articulation, varied configurations and a host of camouflage, concealment and deception tactics. Due to radar cross-section variability, the ability to discriminate between targets also varies greatly with target aspect. Potential space-borne and air-borne sensor systems may eventually be exploited to provide products to the warfighter at tactically relevant timelines. With such potential systems in place, multiple views of a given target area may be available to support targeting. In this paper, we examine the aspect dependence of SAR target classification and develop a Bayesian classification approach that exploits multiple incoherent views of a target. We further examine several practical issues in the design of such a classifier and consider sensitivities and their implications for sensor planning. Experimental results indicating the benefits of aspect diversity for improving performance under extended operating conditions are shown using publicly released 1-foot SAR data from DARPA's MSTAR program.

Paper Details

Date Published: 12 September 2003
PDF: 10 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.487171
Show Author Affiliations
Myron Z. Brown, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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