Progress In Electromagnetics Research B
ISSN: 1937-6472
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 49 > pp. 215-234


By H. Song, W. Yang, X. Xu, and M. Liao

Full Article PDF (718 KB)

We introduce a data-driven unsupervised classification algorithm that uses polarimetric and interferometric synthetic aperture radar (PolInSAR) data. The proposed algorithm uses a classification method that preserves scattering characteristics. Our contribution is twofold. First, the method applies adaptive model-based decomposition (AMD) to represent the scattering mechanism, which overcomes the flaws introduced by Freeman decomposition. Second, a new class initialization scheme using a histogram clustering algorithm based on a Dirichlet process mixture model is applied to automatically determine the number of clusters and effectively initialize the classes. Therefore, our algorithm is data-driven. In the first step, the Shannon entropy characteristics of the PolInSAR data are extracted and used to calculate the local histogram features. After applying AMD, pixels are divided into three canonical scattering categories according to their dominant scattering mechanism. The histogram clustering algorithm is applied to each scattering category to obtain the number of classes and initialize them. The iterative Wishart classifier is applied to refine the classification results. Our method not only can obtain promising unsupervised classification results but also can automatically assign the number of classes. Experimental results for E-SAR L-band PolInSAR images from the German Aerospace Center demonstrate the effectiveness of the proposed algorithm.

H. Song, W. Yang, X. Xu, and M. Liao, "Data-Driven Polinsar Unsupervised Classification Based on Adaptive Model-Based Decomposition and Shannon Entropy Characterization," Progress In Electromagnetics Research B, Vol. 49, 215-234, 2013.

1. Kong, J. A., S. H. Yueh, H. H. Lim, R. T. Shin, and J. J. Van Zyl, "Classification of earth terrain using polarimetric synthetic aperture radar images," Progress In Electromagnetics Research, Vol. 3, 327-370, 1990.

2. Lee, J., M. Grunes, and R. Kwok, "Classification of multi-look polarimetric SAR imagery based on complex wishart distribution," International Journal of Remote Sensing, Vol. 15, No. 11, 2299-2311, 1994.

3. Ferro-Famil, L. and E. Pottier, "Dual frequency polarimetric SAR data classification and analysis," Progress In Electromagnetics Research, Vol. 31, 247-272, 2001.

4. Jin, Y.-Q., "Polarimetric scattering modeling and information retrieval of SAR remote sensing --- A review of FDU work," Progress In Electromagnetics Research, Vol. 104, 333-384, 2010.

5. Goodman, N., "Statistical analysis based on a certain multivariate complex gaussian distribution (an introduction)," Annals of Mathematical Statistics, 152-177, 1963.

6. Cloude, S. 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, 1997.

7. Lee, J., M. Grunes, T. Ainsworth, L. Du, D. Schuler, and S. Cloude, "Unsupervised classification using polarimetric decomposition and the complex wishart classifier," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, 2249-2258, 1999.

8. Pottier, E. and J. Lee, "Unsupervised classification scheme of polsar images based on the complex wishart distribution and the H/A/alpha polarimetric decomposition theorem (polarimetric sar)," EUSAR 2000, 265-268, 2000.

9. Van Zyl, J., "Unsupervised classification of scattering behavior using radar polarimetry data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 27, No. 1, 36-45, 1989.

10. Ferro-Famil, L., E. Pottier, and J. Lee, "Unsupervised classification of natural scenes from polarimetric interferometric SAR data," Frontiers of Remote Sensing Information Processing, 105, 2003.

11. Ferro-Famil, L., E. Pottier, H. Skriver, P. Lumsdon, R. Moshammer, and K. Papathanassiou, "Forest mapping and classification using L-band polinSAR data," ESA Special Publication, Vol. 586, 9, 2005.

12. Cloude, S. 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, 1996.

13. Lee, J., M. Grunes, E. Pottier, and L. Ferro-Famil, "Unsupervised terrain classification preserving polarimetric scattering characteristics," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 4, 722-731, 2004.

14. Freeman, A. and S. Durden, "A three-component scattering model for polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 3, 963-973, 1998.

15. Yang, W., T. Zou, H. Sun, and X. Xu, "Improved unsupervised classification based on freeman-durden polarimetric decomposition," EUSAR 2008, 7th European Conference on Synthetic Aperture Radar, 1-4, VDE, 2008.

16. Van Zyl, J., M. Arii, and Y. Kim, "Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 9, 3452-3459, 2011.

17. Arii, M., J. Van Zyl, and Y. Kim, "Adaptive model-based decomposition of polarimetric SAR covariance matrices," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 3, 1104-1113, 2011.

18. Orbanz, P. and J. Buhmann, "Nonparametric Bayesian image segmentation," International Journal of Computer Vision, Vol. 77, No. 1, 25-45, 2008.

19. Morio, J., P. Refregier, F. Goudail, P. Dubois-Fernandez, and X. Dupuis, "Information theory-based approach for contrast analysis in polarimetric and/or interferometric SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 8, 2185-2196, 2008.

20. Morio, J., P. Refregier, F. Goudail, P. Dubois-Fernandez, and X. Dupuis, "A characterization of shannon entropy and Bhattacharyya measure of contrast in polarimetric and interferometric SAR image," Proceedings of the IEEE, Vol. 97, No. 6, 1097-1108, 2009.

21. Van Zyl, J., "Application of cloude's target decomposition theorem to polarimetric imaging radar data," Proc. SPIE 1748, Radar Polarimetry, 184-191, International Society for Optics and Photonics, 1993.

22. Refregier, P., F. Goudail, P. Chavel, and A. Friberg, "Entropy of partially polarized light and application to statistical processing techniques," Journal of the Optical Society of America A (JOSA A), Vol. 21, No. 11, 2124-2134, 2004.

23. Cloude, S. and K. Papathanassiou, "Polarimetric SAR interferometry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 5, 1551-1565, 1998.

24. Neal, R., "Markov chain sampling methods for dirichlet process mixture models," Journal of Computational and Graphical Statistics, Vol. 9, No. 2, 249-265, 2000.

25. Rosenberg, A. and J. Hirschberg, "V-measure: A conditional entropy-based external cluster evaluation measure," Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Vol. 410, 420, 2007.

© Copyright 2010 EMW Publishing. All Rights Reserved