Discontinuity adaptive MRF model for synthetic aperture radar image analysis

  • P. C. Smits
  • S. G. Dellepiane
  • G. Vernazza
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

In this paper, an approach is presented for the reconstruction and analysis of synthetic aperture radar (SAR) images that preserves better fine structures and borders in the image than classical methods, The method uses the discontinuity adaptive MRF model proposed by Li [1] in combination which an observation model that exploits a gamma distribution. This resulted in a new algorithm that is suited to the analysis of SAR images.

Keywords

Synthetic Aperture Radar Markov Random Field Synthetic Aperture Radar Image Observation Model Speckle Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    Li, S.Z. (1995b), Markov Random Field modeling in computer vision, Springer Verlag, New York.Google Scholar
  2. [2]
    Li, S.Z. (1995), Discontinuity-adaptive MRF prior and robust statistics: a comparative study, IEEE Trans. on Pattern Analysis and Machine Intelligence 17(6), 576–586.Google Scholar
  3. [3]
    Rignot, E., Chellappa, R. (1993). Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data, J. Opt. Soc. Am. A, Vol. 10, No. 4, pp. 573–582.Google Scholar
  4. [4]
    Smits P.C. and S. Dellepiane (1996), “Information fusion in a Markov Random Field based image segmentation approach using adaptive neighbourhoods,“ 13th Int. Conf. on Pattern Recognition, Vienna, August 1996, pp. 570–575.Google Scholar
  5. [5]
    Blake (1983), The least disturbance principle and weak constraints, Pattern Recognition Letters, Vol. 1, pp. 393–399.Google Scholar
  6. [6]
    Blake A. and A. Zisserman (1987), Visual reconstruction. Cambridge, MA: MIT Press.Google Scholar
  7. [7]
    Geman S. and Geman D. (1984), Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-6, nov. 1984, pp. 721–741.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • P. C. Smits
    • 1
  • S. G. Dellepiane
    • 1
  • G. Vernazza
    • 2
  1. 1.Dept. Biophysical and Electronic EngUniversity of GenoaGenovaItaly
  2. 2.Dept. Electrical and Electronic EngUniversity of CagliariCagliariItaly

Personalised recommendations