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Proceedings Article

Hyper-parameter selection in non-quadratic regularization-based radar image formation

[+] Author Affiliations
Özge Batu

Sabanci Univ. (Turkey)

Müjdat Çetin

Sabanci Univ. (Turkey) and Massachusetts Institute of Technology

Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697009 (April 15, 2008); doi:10.1117/12.782341
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From Conference Volume 6970

  • Algorithms for Synthetic Aperture Radar Imagery XV
  • Edmund G. Zelnio; Frederick D. Garber
  • Orlando, FL | March 16, 2008

abstract

We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein's unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) "Backhoe Data Dome," to demonstrate and discuss the effectiveness of these methods.

© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Özge Batu and Müjdat Çetin
"Hyper-parameter selection in non-quadratic regularization-based radar image formation", Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697009 (April 15, 2008); doi:10.1117/12.782341; http://dx.doi.org/10.1117/12.782341


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