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PIERS Online
Vol. 3
No. 5
2007
pp: 625-628Iteration Based Polarimetric SAR Image ClassificationJian Yang, Xiaoli She, and Tao Xiong doi:10.2529/PIERS061004064550 Downloads: 3520 Abstract:In this paper, an iteration method is proposed for supervised polarimetric synthetic aperture radar (SAR) image classification. In this iterative approach, the optimization of polarimetric contrast enhancement (OPCE) is employed for enlarging the distance between the mean values of two kinds of targets and the Fisher method is employed for reducing the variances of two distributions. Using the proposed approach, polarimetric SAR image can be classified only after a few iterations. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L-band image over San Francisco demonstrate the effectiveness of the proposed approach.References:1. Lee, J.-S., M. R. Grunes, and R. Kwok, "Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution," Int. J. Remote Sensing, Vol. 15, No. 11, 2299-2311, 1994. 2. Chen, K. S., W. P. Huang, and et al., "Classification of multifrequency polarimtric SAR imagery using a dynamic learning neural network," IEEE Trans. Geosci. Remote Sensing, Vol. 34, No. 3, 814-820, 1996. 3. Ito, Y. and S. Omatu, "Polarimetric SAR data classification using competitive neural networks," International Journal of Remote Sensing, Vol. 19, No. 14, 2665-2684, 1998. 4. Fukuda, S., R. Kataqiri, and H. Hirosawa, "Unsupervised approach for polarimetric SAR image classification using support vector machines," International Geoscience and Remote Sensing Symposium (IGARSS), Vol. 5, 2599-2601, 2002. 5. Chen, C.-T., K.-S. Chen, and J.-S. Lee, "The use of fully polarimetric information for the fuzzy neural classification of SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 9, PART I, 2089-2100, 2003. 6. Du, L. and J.-S. Lee, "Fuzzy classification of earth terrain covers using complex polarimetric SAR data," International Journal of Remote Sensing, Vol. 17, No. 4, 809, 1996. 7. Van Zyl, J. J., "Unsupervised classification of scattering behavior using radar polarimetry data," IEEE Trans. Geosci. Remote Sensing, Vol. 27, No. 1, 36-45, Jan., 1989. 8. Kong, J. A., A. A. Swartz, H. A. Yueh, and et al., "Identification of terrain cover using the optimal terrain classifier," J. Electronmagn. Waves Applicat., Vol. 2, 171-194, 1988. 9. Yueh, H. A., A. A. Swartz, J. A. Kong, and et al., "Optimal classification of terrain cover using normalized polarimetric data," J. Geophys. Res., 15261-15267. 10. Van Zyl, J. J. and C. F. Burnette, "Bayesian classification of polarimetric SAR images using adaptive a priori probabilities," Int. J. Remote Sensing, Vol. 13, No. 5, 835-840, 1992. 11. Cloude, S. R. and E. Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Trans. Geosci. Remote Sensing, Vol. 35, No. 1, 68-78, Jan., 1997. 12. Lee, J.-S., M. R. Grunes, and et al., "Unsupervised classification using polarimetric decomposition and the complex Wishart classifier," IEEE Trans. Geosci. Remote Sensing, Vol. 37, No. 5, 2249-2257, Sep., 1999. 13. Lee, J.-S. and K. Hoppel, "Principal component transformation of multifrequency polarimetric SAR imagery," IEEE Trans. Geosci. Remote Sensing, Vol. 30, No. 4, 686-696, July, 1992. 14. Lee, J.-S., M. R. Grunes, and G. F. de Grandi, "Polarimetric SAR speckle filtering and its implication for classification," IEEE Trans. Geosci. Remote Sensing, Vol. 37, No. 5, 2363-2373, Sep., 1999. 15. Alberqa, V., "Comparison of polarimetric methods in image classification and SAR interferometry applications," Ph.D. Thesis, DLR, Deutschen Forschungsanstalt fur Luft-und Raumfahrt, Germany, 2004. 16. Yang, J., Y. N. Peng, and S. M. Lin, "Similarity between two scattering matrices," Electron. Letters, Vol. 37, No. 3, 193-194, Feb., 2001. 17. Yang, J., G. W. Dong, Y. N. Peng, Y. Yamaguchi, and Y. Yamada, "Generalized optimization of polarimetric contrast enhancement," IEEE Geosci. Remote Sensing Lett., Vol. 1, No. 3, 171-174, 2004. 18. Yang, J., Y. Yamaguchi, W.-M. Boerner, and S. M. Lin, "Numerical methods for solving the optimal problem of contrast," IEEE Trans. Geosci. Remote Sensing, Vol. 38, No. 2, 965-971, 2000. 19. Yang, J., T. Xiong, Y. Peng, and et al., "Polarimetric SAR image classification by using generalized optimization of polarimetric contrast enhancement," International Journal of Remote Sensing, Vol. 27, No. 16, 3413-3424, 2006. |
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