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Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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CLASSIFICATION OF MULTI-TEMPORAL SAR IMAGES FOR RICE CROPS USING COMBINED ENTROPY DECOMPOSITION AND SUPPORT VECTOR MACHINE TECHNIQUE

By C.-P. Tan, J.-Y. Koay, K.-S. Lim, H.-T. Ewe, and H.-T. Chuah

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Abstract:
This paper presents a combined Entropy Decomposition and Support Vector Machine (EDSVM) technique for Synthetic Aperture Radar (SAR) image classification with the application on rice monitoring. The objective of this paper is to assess the use of multi-temporal data for the supervised classification of rice planting area based on different schedules. Since adequate priori information is needed for this supervised classification, ground truth measurements of rice fields were conducted at Sungai Burung, Selangor, Malaysia for an entire season from the early vegetative stage of the plants to the ripening stage. The theoretical results of Radiative Transfer Theory based on the ground truth parameters are used to define training sets of the different rice planting schedules in the feature space of Entropy Decomposition. The Support Vector Machine is then applied to the feature space to perform the image classification. The effectiveness of this algorithm is demonstrated using multi-temporal RADARSAT-1 data. The results are also used for comparison with the results based on information of training sets from the image using Maximum Likelihood technique, Entropy Decomposition technique and Support Vector Machine technique. The proposed method of EDSVM has shown to be useful in retrieving polarimetric information for each class and it gives a good separation between classes. It not only gives significant results on the classification, but also extends the application of Entropy Decomposition to cover multi-temporal data. Furthermore, the proposed method offers the ability to analyze single-polarized, multi-temporal data with the advantage of the unique features from the combined method of Entropy Decomposition and Support Vector Machine which previously only applicable to multipolarized data. Classification based on theoretical modeling is also one of the key components in this proposed method where the results from the theoretical models can be applied as the input of the proposed method in order to define the training sets.

Citation: (See works that cites this article)
C.-P. Tan, J.-Y. Koay, K.-S. Lim, H.-T. Ewe, and H.-T. Chuah, "Classification of Multi-Temporal SAR Images for Rice Crops Using Combined Entropy Decomposition and Support Vector Machine Technique," Progress In Electromagnetics Research, Vol. 71, 19-39, 2007.
doi:10.2528/PIER07012903
http://www.jpier.org/PIER/pier.php?paper=07012903

References:
1. Le Toan, T., H. Laur, E. Mougin, and A. Lopes, "Multitemporal and dual polarization observations of agricultural vegetation covers by X-band SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 27, 709-717, 1989.
doi:10.1109/TGRS.1989.1398243

2. Le Toan, T., F. Ribbes, L. F. Wang, N. Floury, K. H. Ding, J. A. Kong, M. Fujita, and T. Kurosu, "Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, 41-56, 1997.
doi:10.1109/36.551933

3. Ribbes, F. and T. Le Toan, "Rice field mapping and monitoring with RADARSAT data," International Journal of Remote Sensing, Vol. 20, No. 4, 745-765, 1999.
doi:10.1080/014311699213172

4. Shao, Y., X. Fan, H. Liu, J. Xiao, S. Ross, B. Brisco, R. Brown, and G. Staples, "Rice monitoring and production estimation using multitemporal RADARSAT," Remote Sensing of Environment, Vol. 76, 310-325, 2001.
doi:10.1016/S0034-4257(00)00212-1

5. Wang, L., J. A. Kong, K. H. Ding, T. Le Toan, F. Ribbes-Ballarin, and N. Floury, "Electromagnetic scattering model for rice canopy based on Monte Carlo simulation," Progress In Electromagnetics Research, Vol. 52, 153-171, 2005.
doi:10.2528/PIER04080601

6. 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, 1997.
doi:10.1109/36.551935

7. Cloude, S. R., J. Fortuny, J. M. Lopez-Sanchez, and A. J. Sieber, "Wide-band polarimetric radar inversion studies for vegetation layers," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, 2430-2441, 1999.
doi:10.1109/36.789640

8. Lopez-Martinez, C., E. Pottier, and S. R. Cloude, "Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 9, 2058-2074, 2005.
doi:10.1109/TGRS.2005.853934

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, 1996.
doi:10.1109/36.485127

10. Lee, J. S., M. R. Grunes, and T. L. Ainsworth, "Unsupervised classification using polarimetric decomposition and the complex wishart distribution," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, 2249-2259, 1999.
doi:10.1109/36.789621

11. Ferro-Famil, L. and E. Pottier, "Dual frequency polarimetric SAR DATA classification and analysis," Progress In Electromagnetics Research, Vol. 31, 247-272, 2001.
doi:10.2528/PIER00081601

12. Yahia, M. and Z. Belhadj, Unsupervised classification of polarimetric SAR images using neural networks, IEEE International Conference Information and Communication Technologies 2004, 335-337, 2004.

13. Fukada, S. and H. Hirosawa, Support vector machine classification of land cover: Application to polarimetric SAR data, IGARRS 2001, 2001.

14. Pal, M. and P. M. Mather, "Assessment of the effectiveness of support vector machines for hyperspectral data," Future Generation Computer Systems, 1215-1225, 2004.
doi:10.1016/j.future.2003.11.011

15. Angiulli, G., V. Barrile, and M. Cacciola, "SAR imagery classification using multi-class support vector machines," Journal of Electromagnetic Waves and Applications, Vol. 19, No. 14, 1865-1872, 2005.
doi:10.1163/156939305775570558

16. Mercier, G. and F. Firard-Ardhuin, Oil slick detection by SAR imagery using support vector machines, Proc. of the IEEE Europe, Vol. 1, 90-95, 2005.

17. Camps-Valls, G., L. G´omez-Chova, J. Calpe, E. Soria, J. D. Martin, L. Alonso, and J. Moreno, "Robust support vector technique for hyperspectral data classification and knowledge discovery," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 7, 1530-1542, 2004.
doi:10.1109/TGRS.2004.827262

18. Camps-Valls, G. and L. Bruzzone, "Kernel-based techniques for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 6, 1351-1362, 2005.
doi:10.1109/TGRS.2005.846154

19. Christianini, N. and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge Univ. Press, Cambridge, U.K., 2000.

20. Ewe, H. T. and H. T. Chuah, "Electromagnetic scattering from an electrically dense vegetation medium," IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5, 2093-2105, 2000.
doi:10.1109/36.868868

21. Vapnik, V., The Nature of Statistical Learning Theory, Springer Verlag, 1999.

22. Schölkopf, B. and A. J. Smola, Learning With Kernels, The MIT Press, Cambridge, Matssachusetts, London, England, 2002.

23. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801

24. Vapnik, V., The Nature of Statistical Learning Theory, Springer Verlag, 1995.

25. Huang, C., L. S. Davis, and J. R. G. Townshend, "An assessment of support vector machine for land cover classification," International. Journal of Remote Sensing, Vol. 23, 725-749, 2002.
doi:10.1080/01431160110040323

26. Knerr, C., L. Personnaz, and G. Dreyfus, "Single-layer learning revisited: a stepwise procedure for building and training a neural network," Neurocomputing: Algorithms, 1990.

27. Friedman, J., "Another approach to polychotomous classification," Technical report, 1996.

28. Kreßel, U., "Pairwise classification and support vector machines," Advances in Kernel Methods — Support Vector Learning, 1999.

29. Hsu, C. W. and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, Vol. 13, No. 2, 415-425, 2002.
doi:10.1109/72.991427

30. Chuah, H. T., S. Tjuatja, A. K. Fung, and J. W. Bredow, "Radar backscatter from a dense discrete random medium," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 4, 892-900, 1997.
doi:10.1109/36.602531

31. Albert, M. D., T. E. Tan, H. T. Ewe, and H. T. Chuah, "A theoretical and measurement study of sea ice and ice shelf in Antarctica as electrically dense media," Journal of Electromagnetic Waves and Applications, Vol. 19, No. 14, 1973-1981, 2005.
doi:10.1163/156939305775570639

32. Ewe, H. T. and H. T. Chuah, "A study of Fresnel scattered field for non-spherical discrete scatterers," Progress In Electromagnetics Research, Vol. 25, 189-222, 2000.
doi:10.2528/PIER99060701

33. Chandrasekhar, S., Radiative Transfer, Dover, New York, 1960.

34. Koay, J. Y., C. P. Tan, H. T. Ewe, H. T. Chuah, and S. Bahari, Theoretical modeling and measurement comparison of season-long rice field monitoring, Proceedings of Progress In Electromagnetics Research Symposium 2005, 22-26, 2005.

35. Ulaby, F. T., R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive, Vol. III, Vol. III, Artech House, Norwood, MA, 1986.

36. Lopes, A., E. Mougin, T. Le Toan, M. A. Karam, and A. K. Fung, "A simulation study on the influence of leaf orientation on elliptically polarized microwave propagation in a coniferous forest," Journal of Electromagnetic Waves and Applications, Vol. 5, No. 7, 753-776, 1991.


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