Statistical Modeling of Multi-channel SAR Images



Currently, with the advancement of sophisticated SAR imaging modes, such as multitemporal interferometry and polarimetry, the return backscatter results are presented in two or more channels [1, 2]. The SAR interferogram, achieved by multiplying the first image by the complex conjugate of the second one [3, 4], has become an important tool for multiple-channel SAR image interpretation.


  1. 1.
    H. Steyskal, J.K. Schindler, P. Franchi, R.J. Mailloux, Pattern synthesis for TechSat21-A distributed space-based radar system. IEEE Antennas Propag. Mag. 45(4), 19–25 (2003)CrossRefGoogle Scholar
  2. 2.
    T. Wang, Z. Bao, Z. Zhang, J. Ding, Improving coherence of complex image pairs obtained by along-track bistatic SARs using range-azimuth prefiltering. IEEE Trans. Geosci. Remote Sens. 46(1), 3–13 (2008)CrossRefGoogle Scholar
  3. 3.
    J.S. Lee, K.W. Hoppel, S.A. Mango, A.R. Miller, Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery. IEEE Trans. Geosci. Remote Sens. 32(5), 1017–1028 (1994)CrossRefGoogle Scholar
  4. 4.
    G. Ferraiuolo, G. Poggi, A Bayesian filtering technique for SAR interferometric phase fields. IEEE Trans. Image Process. 13(10), 1368–1378 (2004)CrossRefGoogle Scholar
  5. 5.
    D.C. Maori, J. Klare, A.R. Brenner, J.H.G. Ender, Wide-area traffic monitoring with the SAR/GMTI system PAMIR. IEEE Trans. Geosci. Remote Sens. 46(10), 3019–3030 (2008)CrossRefGoogle Scholar
  6. 6.
    E. Chapin, C.W. Chen, Along-track interferometry for ground moving target indication. IEEE Aerosp. Electron. Syst. Mag. 23(6), 19–24 (2008)CrossRefGoogle Scholar
  7. 7.
    C.H. Gierull, Statistical analysis of multilook SAR interferograms for CFAR detection of ground moving targets. IEEE Trans. Geosci. Remote Sens. 42(4), 691–701 (2004)CrossRefGoogle Scholar
  8. 8.
    N.R. Goodman, Statistical analysis based on a certain multivariate complex gaussian distribution (an introduction). Ann. Math. Stat. 34(152), 152–180 (1963)MathSciNetCrossRefGoogle Scholar
  9. 9.
    I.C. Sikaneta, Detection of ground moving objects with synthetic aperture radar, Ph. D. thesis. University of Ottawa, 2002Google Scholar
  10. 10.
    R. Touzi, A. Lopes, J. Bruniquel, P.W. Vachon, Coherence estimation for SAR imagery. IEEE Trans. Geosci. Remote Sens. 37(1), 135–149 (1999)CrossRefGoogle Scholar
  11. 11.
    R. Abdelfattah, J.M. Nicolas, Interferometric SAR coherence magnitude estimation using second kind statistics. IEEE Trans. Geosci. Remote Sens. 44(7), 1942–1953 (2006)CrossRefGoogle Scholar
  12. 12.
    C.J. Oliver, S. Quegan, Understanding Synthetic Aperture Radar Images (Artech House, Norwood, MA, 1998)Google Scholar
  13. 13.
    M. Tur, K.C. Chin, J.W. Goodman, When is speckle noise multiplicative? Appl. Opt. 21, 1157–1159 (1982)CrossRefGoogle Scholar
  14. 14.
    A.C. Frery, J. Jacobo-Berlles, J. Gambini, M. Mejail, Polarimetric SAR image segmentation with B-Splines and a new statistical model. Multidimension. Syst. Signal Process. 21, 319–342 (2010)CrossRefGoogle Scholar
  15. 15.
    A.C. Frery, A.H. Correia, C.C. Freitas, Classifying multifrequency fully polarimetric imagery with multiple sources of statistical evidence and contextual information. IEEE Trans. Geosci. Remote Sens. 45(10), 3098–3109 (2007)CrossRefGoogle Scholar
  16. 16.
    A.C. Frery, H.J. Muller, C.C. Freitas Yanasse, S.J. Siqueira Sant’Anna, A model for extremely heterogeneous clutter. IEEE Trans. Geosci. Remote Sens. 35(3), 648–659 (1997)CrossRefGoogle Scholar
  17. 17.
    J.M. Nicolas, F. Tupin, Gamma mixture modeled with second kind statistics: application to SAR image processing. Presented at the IGARSS Conference, Toronto, ON, Canada, 2002, pp. 2489–2491 Google Scholar
  18. 18.
    J.M. Nicolas, Introduction to second kind statistic: Application of log-moments and log-cumulants to SAR image law analysis. Trait. Signal 19(3), 139–167 (2002)zbMATHGoogle Scholar
  19. 19.
    G. Moser, J. Zerubia, S.B. Serpico, SAR amplitude probability density function estimation based on a generalized Gaussian model. IEEE Trans. Image Process. 15(6), 1429–1442 (2006)CrossRefGoogle Scholar
  20. 20.
    C. Tison, J.M. Nicolas, F. Tupin, H. Maitre, A new statistical model for Markovian classification of urban areas in high-resolution SAR images. IEEE Trans. Geosci. Remote Sens. 42(10), 2046–2057 (2004)CrossRefGoogle Scholar
  21. 21.
    S.I. Gradshteyn, I.M. Ryzhik, Table of Integrals, Series, and Products, 7th edn. (Academic Press, San Diego, CA, 2007)Google Scholar
  22. 22.
    M.S. Greco, G. Gini, Statistical analysis of high-resolution SAR ground clutter data. IEEE Trans. Geosci. Remote Sens. 45(3), 566–575 (2007)CrossRefGoogle Scholar
  23. 23.
    Y. Delignon, W. Pieczynski, Modelling non-Rayleigh speckle distribution in SAR images. IEEE Trans. Geosci. Remote Sens. 40(6), 1430–1435 (2002)CrossRefGoogle Scholar
  24. 24.
    A. Achim, E.E. Kuruoglu, J. Zerubia, SAR image filtering based on the heavy-tailed Rayleigh model. IEEE Trans. Image Process. 15(9), 2686–2693 (2006)CrossRefGoogle Scholar
  25. 25.
    E.E. Kuruoglu, J. Zerubia, Modeling SAR images with a generalization of the Rayleigh distribution. IEEE Trans. Image Process. 13(4), 527–533 (2004)CrossRefGoogle Scholar
  26. 26.
    K.L.P. Vasconcellos, A.C. Frery, L.B. Silva, Improving estimation in speckled imagery. Comput. Stat. 20(3), 503–519 (2005)MathSciNetCrossRefGoogle Scholar
  27. 27.
    H. Allende, A.C. Frery, J. Galbiati, L. Pizarro, M-estimators with asymmetric influence functions: the GA0 distribution case. J. Stat. Comput. Simul. 76(11), 941–956 (2006)MathSciNetCrossRefGoogle Scholar
  28. 28.
    T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley, New York, 1991)CrossRefGoogle Scholar

Copyright information

© National Defense Industry Press, Beijing and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National University of Defense TechnologyChangshaChina

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