volume | PIER Journals

Abstract

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6×6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 by 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class characteristics. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the crosscorrelation matrix. The analysis of the resulting clusters is realized by determining the rigorous change in polarimetric properties from one image to the other. The polarimetric variations are parameterized by 8 real coefficients derived from the decomposition of a special unitary operator on the Gell-Mann basis. These classification and analysis schemes are applied to full polarimetric P, L, and C bands SAR images of the Nezer forest acquired by NASA/JPL AIRSAR sensor (1989).

1. Rignot, E., R. Chellappa, and P. Dubois, "Unsupervised segmentation of polarimetric SAR data using the covariance matrix," IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 4, 697-705, July 1992.
doi:10.1109/36.158863 Google Scholar

2. Zebker, H. A., J. J. van Zyl, S. L. Durden, and L. Norikane, "Calibrated imaging radar Polarimetry: techniques examples and applications," IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, 942-961, 1991.
doi:10.1109/36.101373 Google Scholar

3. Hara, R., G. Atkins, S. H. Yueh, R. T. Shin, and J. A. Kong, "Application of neural networks to radar image classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, 100-110, January 1994.
doi:10.1109/36.285193 Google Scholar

4. van Zyl, J. J. and C. F. Burnette, "Bayesian classification of polarimetric SAR images using adaptive a-priori probabilities," International Journal of Remote Sensing, Vol. 13, 835-840, 1992.
doi:10.1080/01431169208904157 Google Scholar

5. Pottier, E., "On full polarimetric target decomposition theorems with application to classification and identification of real target cross section," Proceedings of International Radar Conference, 330-335, Paris, May 1994. Google Scholar

6. van Zyl, J. J., "Unsupervised classification of scattering behavior using radar polarimetry data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 27, 36-45, 1989.
doi:10.1109/36.20273 Google Scholar

7. Boerner, W. M., et al. "Polarimetry in radar remote sensing: basic and applied concepts," Chapter 5 Principles and Applications of Imaging Radar, The Manual of Remote Sensing, 3rd Edition, The American Society for Photogrammetry and Remote Sensing, March 1998. Google Scholar

8. Huynen, J. R., "Phenomenological theory of radar targets,", Ph.D. dissertation, Drukkerij Bronder-offset, N. V. Rotterdam, 1970. Google Scholar

9. Cloude, S. R. and E. Pottier, "A review of target decomposition theorems in radar polarimetry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 2, 498-518, September 1995.
doi:10.1109/36.485127 Google Scholar

10. Cloude, S. R. and E. Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 1, 68-78, January 1997.
doi:10.1109/36.551935 Google Scholar

11. Freeman, A. and S. Durden, "A three component scattering model to describe polarimetric SAR data," SPIE, Vol. 1748, 213-225, Radar Polarimetry, 1992. Google Scholar

12. Dong, Y., B. Forster, and C. Ticehurst, "A new decomposition of radar polarization signatures," IEEE Trans. Geoscience and Remote Sensing, Vol. 36, No. 3, 933-939, 1998.
doi:10.1109/36.673684 Google Scholar

13. Shi, J. and J. Dozier, "On estimation of snow water equivalence using SIR-C/X-SAR," proceedings of the Second International Workshop on Retrieval of Bio- and Geo-physical Parameters from SAR Data for Land Application, Noordwijk, The Netherlands, October 1998. Google Scholar

14. Floricioiu, D. M., "Polarimetric signatures and classification of alpine terrain by means of SIR-C/X-SAR,", Ph.D. dissertation, Innsbruck, Austria, 1997. Google Scholar

15. Chen, K. S., et al. "Classification of multifrequency polarimetric SAP, image using a dynamic learning neural network," IEEE Trans. Geoscience and Remote Sensing, Vol. 34, No. 3, 814-820, 1996.
doi:10.1109/36.499786 Google Scholar

16. Freeman, A., S. Durden, and R. Zimmerman, "Mapping Sub- Tropical vegetation using Multi-Frequency Multi-Polarization SAR data," Proceedings of IGARSS, 1686-1689, Houston, USA, June 1992. Google Scholar

17. Lee, J. S., M. R. Grunes, and R. Kwok, "Classification of multilook polarimetric SAR imagery based on the complex Wishart distribution," International Journal of Remote Sensing, Vol. 15, No. 11, 2299-2311, 1994.
doi:10.1080/01431169408954244 Google Scholar

18. Kong, J. A., S. H. Yueh, R. T. Shin, and J. J. van Zyl, "Classification of earth terrain using polarimetric synthetic aperture radar images," Chapter 6 in PIER, Vol. 3, J. A. Kong (Ed.), Elsevier 1990. Google Scholar

19. Lee, J. S., M. R. Grunes, T. L. Ainsworth, L. Du, D. L. Schuler, and S. R. Cloude, "Unsupervised classification of polarimetric SAR images by applying target decomposition and complex wishart distribution," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, 2249-2258, Sept. 1999, also proceedings of the Fourth International Workshop on Radar Polarimetry, PIERS 1998, 13–17, Nantes, France, July 1998.
doi:10.1109/36.789621 Google Scholar

20. Cloude, S. R. and K. P. Papathanassiou, "Polarimetric SAR interferometry," IEEE Trans. Geoscience and Remote Sensing, Vol. 36, No. 5, 1551-1565, 1998.
doi:10.1109/36.718859 Google Scholar

21. Ferro-Famil, L., E. Pottier, J. P. Dedieu, C. Corgier, and J. Saillard, "Application of polarimetric SAR data processing to snow cover remote sensing. Validation using optical images and ground data," proceedings of the Committee on Earth Observing Satellites SAR Workshop, 26-29, CNES, Toulouse, France, October 1999. Google Scholar

22. Goodman, N. R., "Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction)," Ann. Math. Statist., Vol. 34, 152-177, 1963.
doi:10.1214/aoms/1177704250 Google Scholar

23. Muirhead, R. J., Aspects of Multivariate Statistical Theory, John Wiley and Sons, New-York, ISBN 0-471-094442-0.

24. Pottier, E. and J. S. Lee, "Application of the H/A/alpha polarimetric decomposition theorem for unsupervised classification of fully polarimetric SAR data based on the wishart distribution," proceedings of the Committee on Earth Observing Satellites SAR Workshop, 26-29, CNES, Toulouse, France, October 1999. Google Scholar

25. Cloude, S. R., "Group theory and polarization algebra," OPTIK, Vol. 75, No. 1, 26-36, January 1986. Google Scholar

26. Joshi, A. W., Elements of Group Theory for Physicists, Wiley Eastern Limited, New Delhi, September 1988.

27. Cloude, S. R., "Lie groups in electromagnetic wave propagation and scattering," Journal of Electromagnetic Waves and Applications, Vol. 6, No. 8, 947-974, 1992. Google Scholar

28. Cloude, S. R. and E. Pottier, "Matrix difference operators as classifiers in polarimetric radar imaging," L’Onde Electrique, Vol. 74, No. 3, 34-40, May–June 1994. Google Scholar

Prof. Laurent Ferro-Famil

Dr. Eric Pottier