Superresolution SAR image formation via parametric spectral estimation methods

Zhaoqiang Bi; Jian Li; Zheng-She Liu

doi:10.1117/12.321828

15 September 1998 Superresolution SAR image formation via parametric spectral estimation methods

Zhaoqiang Bi, Jian Li, Zheng-She Liu

Proceedings Volume 3370, Algorithms for Synthetic Aperture Radar Imagery V; (1998) https://doi.org/10.1117/12.321828
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Zhaoqiang Bi, Jian Li, and Zheng-She Liu "Superresolution SAR image formation via parametric spectral estimation methods", Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); https://doi.org/10.1117/12.321828

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available