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Proceedings Paper

Superresolution SAR image formation via parametric spectral estimation methods
Author(s): Zhaoqiang Bi; Jian Li; Zheng-She Liu
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Paper Abstract

This paper considers super resolution synthetic aperture radar (SAR) image formation via sophisticated parametric spectral estimation algorithms. Parametric spectral estimation methods are devised based on parametric data models and are used to estimate the model parameters. Since SAR images rather than model parameters are often more appreciated in SAR applications, we use the parameter estimates obtained with the parametric methods to simulate data matrices of large dimensions and then use the fast Fourier transform (FFT) methods on them to generate SAR images with super resolution. Experimental examples using the MSTAR and ERIM data illustrate that robust spectral estimation algorithms can generate SAR images of higher resolution that the conventional FFT methods and enhance the dominant target features.

Paper Details

Date Published: 15 September 1998
PDF: 12 pages
Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); doi: 10.1117/12.321828
Show Author Affiliations
Zhaoqiang Bi, Univ. of Florida (United States)
Jian Li, Univ. of Florida (United States)
Zheng-She Liu, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 3370:
Algorithms for Synthetic Aperture Radar Imagery V
Edmund G. Zelnio, Editor(s)

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