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Detail-Preserving Processing of Remote Sensing Images

  • Silvana Dellepiane
Conference paper

Summary

This paper deals with the problem of detail-preserving processing of remotely sensed images. It describes two approaches based, on the use of the local adaptivity properties exploited by methods of the contextual type (for instance, the Markov Random Field model [6]) and on the application of fuzzy topology by the so-called isocontour method [2], respectively.

Keywords

Synthetic Aperture Radar Markov Random Field Remote Sensing Image Markov Random Field Model Fuzzy Topology 
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.

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References

  1. 1.
    J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981.Google Scholar
  2. 2.
    S. Dellepiane, F. Fontana, and G. Vernazza, “Non-linear image labelling for multivalued segmentation”, IEEE Transactions on Image Processing, vol. 5, no. 3, pp. 429–446, 1996.CrossRefGoogle Scholar
  3. 3.
    S. Dellepiane and F. Fontana, “Supervised fuzzy contextual segmentation of polarimetric SAR images”, European Transactions on Telecommunications, vol. 6, pp. 515–525, 1996.CrossRefGoogle Scholar
  4. 4.
    R. 0. Duda and P.E. Hart, Pattern classification and Scene Analysis, Wiley Interscience, New York, 1974.Google Scholar
  5. 5.
    A. Freeman, J. Villasenor, J.D. Klein, P. Hoogeboom and J. Groot, “On the use of multi-frequency and polarimetric radar backscatter features for classification of agricultural crops”, International Journal of Remote Sensing, vol. 15, no. 9, pp. 1799–1812, 1994.CrossRefGoogle Scholar
  6. 6.
    S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721–741, 1984.CrossRefGoogle Scholar
  7. 7.
    S.Z. Li, “On discontinuity-adaptive smoothness priors in computer vision”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 6, pp. 576–586, 1995.CrossRefGoogle Scholar
  8. 8.
    S.K. Pal and A. Rosenfeld, “Image enhancement and thresholding by optimization of fuzzy compactness”, Pattern Recognition Letters, vol. 8, pp. 21–28, 1988.CrossRefGoogle Scholar
  9. 9.
    S.K. Pal and A. Ghosh, “Index of area coverage of fuzzy image subsets and object extraction”, Pattern Recognition Letters, vol. 11, pp. 831–841, 1990.CrossRefGoogle Scholar
  10. 10.
    P. Perez and F. Heitz, “Restriction of a Markov random field on a graph and multiresolution statistical image modeling”, IEEE Transactions on Information Theory, vol. 42, no. 1, pp. 180–190, 1996.CrossRefGoogle Scholar
  11. 11.
    E. Rignot and R. Chellappa, “Segmentation of polarimetric synthetic aperture radar data”, IEEE Transactions on Image Processing, vol. 1, no. 3, pp. 281–300, 1992.CrossRefGoogle Scholar
  12. 12.
    A. Rosenfeld, “The fuzzy geometry of image subset”, Pattern Recognition Letters, vol. 2, pp. 311–317, 1984.CrossRefGoogle Scholar
  13. 13.
    P.C. Smits and S.G. Dellepiane, “Synthetic Aperture Radar image segmentation by a detail preserving Markov Random Field approach”, IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 4, pp. 844–857, 1997.CrossRefGoogle Scholar
  14. 14.
    P.C. Smits and S. Dellepiane, “Irregular MRF region label model for multichannel image segmentation”, Pattern Recognition Letters vol. 18, no. 11–13, pp. 1133–1142, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1999

Authors and Affiliations

  • Silvana Dellepiane
    • 1
  1. 1.Department of Biophysical and Electronic Engineering (DIBE)University of GenoaGenovaItaly

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