Advertisement

Polarimetric Synthetic Aperture Radar Data and the Complex Wishart Distribution

  • Allan Aasbjerg Nielsen
  • Knut Conradsen
  • Henning Skriver
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

When working with multi-look fully polarimetric synthetic aperture radar (SAR) data an appropriate way of representing the back-scattered signal consists of the so-called covariance matrix. For each pixel this is a 3×3 Hermitian, positive definite matrix which follows a complex Wishart distribution. Based on this distribution a test statistic for equality of two such matrices and an associated asymptotic probability for obtaining a smaller value of the test statistic are given and applied to segmentation, change detection and edge detection in polarimetric SAR data. In a case study EMISAR L-band data from 17 April 1998 and 20 May 1998 covering agricultural fields near Foulum, Denmark, are used.

Keywords

Covariance Matrix Computer Vision Change Detection Computer Graphic Edge Detection 
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.
    F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive, Artech, Dedham, MA, 1986, vol. 3.Google Scholar
  2. 2.
    H. Skriver, M. T. Svendsen, and A. G. Thomsen, “Multitemporal L-and C-band polarimetric signatures of crops,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, pp. 2413–2429, 1999.CrossRefGoogle Scholar
  3. 3.
    J. J. van Zyl and F. T. Ulaby, “Scattering matrix representation for simple targets,” in Radar Polarimetry for Geoscience Applications, F. T. Ulaby and C. Elachi, Eds. Artech, Norwood, MA, 1990.Google Scholar
  4. 4.
    S. V. Nghiem, S. H. Yueh, R. Kwok, and F. K. Li, “Symmetry properties in polarimetric remote sensing,” Radio Science, vol. 27, no. 5, pp. 693–711, 1992.CrossRefGoogle Scholar
  5. 5.
    K. Conradsen, A. A. Nielsen, J. Schou, and H. Skriver, “A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data,” Accepted for IEEE Transactions on Geoscience and Remote Sensing, 2002.Google Scholar
  6. 6.
    K. Conradsen, A. A. Nielsen, H. Skriver, and J. Schou, “Change detection in polarimetric SAR data and the complex Wishart distribution,” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Sydney, New South Wales, Australia, 9–13 July 2001.Google Scholar
  7. 7.
    H. Skriver, A. A. Nielsen, and K. Conradsen, “Evaluation of the Wishart test statistic for polarimetric data,” Invited to the International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, Prance, 21–25 July 2003.Google Scholar
  8. 8.
    J. Schou, H. Skriver, K. Conradsen, and A. A. Nielsen, “CFAR edge detector for polarimetric SAR data,” Accepted for IEEE Transactions on Geoscience and Remote Sensing, 2002.Google Scholar
  9. 9.
    H. Skriver, J. Schou, K. Conradsen, and A. A. Nielsen, “Polarimetric edge detector based on the complex Wishart distribution,” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Sydney, New South Wales, Australia, 9–13 July 2001.Google Scholar
  10. 10.
    J. Schou, Feature Extraction for Topographic Mapping, Ph.D. thesis, Ørsted•DTU, Technical University of Denmark, Lyngby, 2001.Google Scholar
  11. 11.
    H. Skriver, J. Schou, A. A. Nielsen, and K. Conradsen, “Polarimetric segmentation using the complex Wishart test statistic,” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Ontario, Canada, 24–28 June 2002.Google Scholar
  12. 12.
    R. Touzi, A. Lopes, and P. Bousquez, “A statistical and geometrical edge detector for SAR images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 6, pp. 764–773, Nov. 1988.CrossRefGoogle Scholar
  13. 13.
    A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and statistical adaptive speckle filtering in SAR images,” International Journal of Remote Sensing, vol. 13, no. 9, pp. 1735–1758, 1993.CrossRefGoogle Scholar
  14. 14.
    S. N. Madsen, E. L. Christensen, N. Skou, and J. Dall, “The Danish SAR system: Design and initial tests,” IEEE Transactions on Geoscience and Remote Sensing, vol. 29, pp. 417–476, 1991.CrossRefGoogle Scholar
  15. 15.
    E. L. Christensen, N. Skou, J. Dall, K. Woelders, J. H. J0rgensen, J. Granholm, and S. N. Madsen, “EMISAR: An absolutely calibrated polarimetric L-and C-band SAR,” IEEE Transactions on Geoscience and Remote Sensing, vol. 36, pp. 1852–1865, 1998.CrossRefGoogle Scholar
  16. 16.
    W. Dierking, J. Schou, and H. Skriver, “Change detection of small objects and linear features in multi-temporal polarimetric images,” in Proceedings of the In ternational Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Ed., Honolulu, Hawaii, USA, 24–28 July 2000, pp. 1693–1695.Google Scholar
  17. 17.
    R. Cook, I. McConnell, and C. Oliver, “MUM (Merging Using Moments) segmentation for SAR images,” in SPIE vol. 2316, 1994, pp. 92–103.CrossRefGoogle Scholar
  18. 18.
    C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, 1998.Google Scholar
  19. 19.
    W. Dierking and H. Skriver, “Change detection for thematic mapping by means of airborne multi-temporal polarimetric SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 3, pp. 618–636, 2002.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Allan Aasbjerg Nielsen
    • 1
  • Knut Conradsen
    • 1
  • Henning Skriver
    • 2
  1. 1.IMM, Informatics and Mathematical ModellingTechnical University of DenmarkKgs. LyngbyDenmark
  2. 2.EMI, Section for Electromagnetic Systems, Ørsted•DTUTechnical University of DenmarkKgs. LyngbyDenmark

Personalised recommendations