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Proceedings Paper

Multi-aspect target detection for SAR imagery using hidden Markov models and two-dimensional matching pursuits
Author(s): Paul R. Runkle; Lam H. Nguyen; Lawrence Carin
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Paper Abstract

Radar scattering from an illuminated object is often dependent on target-sensor orientation. In synthetic aperture radar (SAR) imagery, the aspect dependence of the target over the aperture is lost during image formation. To recover this directional dependence, we post-processes the SAR imagery to generate a sequence of images over a corresponding sequence of subapertures. Features are extracted from the sequence of subaperture images using a two-dimensional matching pursuits algorithm. The feature statistic associated with geometrically distinct target-sensor orientations are then used to design a hidden Markov model (HMM) for the target class. This approach explicitly incorporates the sensor motion into the model and accounts for the fact that the orientation of the target is assumed to be unknown. Performance is quantified by considering the detection of tactical targets concealed in foliage.

Paper Details

Date Published: 24 August 2000
PDF: 9 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396337
Show Author Affiliations
Paul R. Runkle, Duke Univ. (United States)
Lam H. Nguyen, Army Research Lab. (United States)
Lawrence Carin, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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