Journal of Central South University

, Volume 18, Issue 3, pp 809–815 | Cite as

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

  • Zheng-wei Zhu (朱正为)Email author
  • Jian-jiang Zhou (周建江)


A super-resolution reconstruction approach of radar image using an adaptive-threshold singular value decomposition (SVD) technique was presented, and its performance was analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function (PSF) matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar (SAR) images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio (SNR). Five versions of SVD algorithms, namely 1) using all singular values, 2) using the top 80% singular values, 3) using the top 50% singular values, 4) using the top 20% singular values and 5) using singular values s such that s2≥max(s2)/rinSNR were tested. The experimental results indicate that when the singular value threshold is set as smax/(rinSNR)1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

Key words

synthetic-aperture radar image reconstruction super-resolution singular value decomposition adaptive-threshold 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    ZHU Zheng-wei, ZHOU Jian-jiang. Optimum selection of common master image for ground deformation monitoring based on PS-DInSAR technique [J]. Journal of Systems Engineering and Electronics, 2009, 20(6): 1213–1220.Google Scholar
  2. [2]
    WANG Zheng-ming, ZHU Ju-bo. SAR image resolution improvement technology [M]. 1st ed. Beijing: Science Press, 2006: 1–9. (in Chinese)Google Scholar
  3. [3]
    SULLIVAN R J. Radar foundations for imaging and advanced concepts [M]. 1st ed. Raleigh: SciTech Publishing Inc, 2004: 155–169.Google Scholar
  4. [4]
    LANE R O, COPSEY K D, WEBB A R. A Bayesian approach to simultaneous autofocus and super-resolution [C]// Proceedings of SPIE. Denver: SPIE Press, 2004: 133–142.Google Scholar
  5. [5]
    SAMSONOV A, BLOCK W F, FIELD A S. Reconstruction of MRI data using sparse matrix inverses [C]// The Forty-First Asilomar Conference on Signals, Systems and Computers. Pacific Grove: IEEE Press, 2007: 1884–1887.Google Scholar
  6. [6]
    SELÉN Y, STOICA P. Estimation of semi-sparse radar range profiles [J]. Digital Signal Processing, 2008, 18(4): 543–560.CrossRefGoogle Scholar
  7. [7]
    KIM K T, SEO D K, AND KIM H T. Efficient radar target recognition using the MUSIC algorithm and invariant features [J]. IEEE Transactions on Antennas and Propagation, 2002, 50(3): 325–337.CrossRefGoogle Scholar
  8. [8]
    THOMPSON P, NANNINI M, SCHEIBER R. Target separation in SAR image with the MUSIC algorithm [C]// IEEE International Geoscience and Remote Sensing Symposium, Barcelona: IEEE Press, 2007: 468–471.Google Scholar
  9. [9]
    LANE R O, COPSEY K D, WEBB A R. Assessment of a Bayesian approach to recognizing relocatable targets [C]// NATO RTO SET-096 Specialists’ Meeting on the Millimeterwave Advanced Target Recognition and Identification Experiment. Oberammergau: IEEE Press, 2005: 1–12.Google Scholar
  10. [10]
    RODRIGUEZ J L, TABOADA J M, ARAUJO M G, OBELLEIRO BASTEIRO F, LANDESA L, GARCIA TUNON J I. On the use of the singular value decomposition in the fast multipole method [J]. IEEE Transactions on Antennas and Propagation, 2008, 56(8): 2325–2344.MathSciNetCrossRefGoogle Scholar
  11. [11]
    ABUJARAD F, NADIM G, OMAR A. Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (SVD) [C]// Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar. Delft: IEEE Press, 2005: 37–42.Google Scholar
  12. [12]
    LUTTRELL S P. Prior knowledge and object reconstruction using the best linear estimate technique [J]. Optica Acta, 1985, 32(6): 703–716.CrossRefGoogle Scholar
  13. [13]
    YANG Jie, SARKAR T K. Interpolation/extrapolation of radar cross-section (RCS) data in the frequency domain using the Cauchy method [J]. IEEE Transactions on Antennas and Propagation, 2007, 55(10): 2844–2851.CrossRefGoogle Scholar
  14. [14]
    MǎDǎLINA M D, DIRK T. Evolutionary Markov chain Monte Carlo [M]. New York: Springer Berlin/Heidelberg, 2004: 63–76.zbMATHGoogle Scholar
  15. [15]
    CHIB S, GREENBERG E. Understanding the Metropolis-Hastings algorithm[J]. The American Statistician, 1995, 49(4): 327–335.Google Scholar
  16. [16]
    COPSEY K D, LANE R O, WEBB A R. Designing NCTR algorithms when operating sensor conditions differ from training conditions [C]// International Conference on Radar Systems. Toulouse: SPIE Press, 2004: 126–131.Google Scholar

Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zheng-wei Zhu (朱正为)
    • 1
    • 2
    Email author
  • Jian-jiang Zhou (周建江)
    • 1
  1. 1.College of Information Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Information EngineeringSouthwest University of Science and TechnologyMianyangChina

Personalised recommendations