Polarimetric SAR image classification using radial basis function neural network

PIERS 2010 Cambridge - Progress in Electromagnetics Research Symposium, Proceedings

Volumn , Issue , 2010, Pages 60-65

  Ince, Turker ^a  

^a IZMIR UNIVERSITY OF ECONOMICS   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

AIRBORNE SAR; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CLASSIFIER TRAINING; CONFUSION MATRICES; DATA SETS; EXPERIMENTAL STUDIES; FEATURE SETS; FEATURE SPACE; GRAY LEVEL CO-OCCURRENCE MATRIX; PIXEL VALUES; POLARIMETRIC SAR; POLARIMETRIC SYNTHETIC APERTURE RADARS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RBF NETWORK; SAN FRANCISCO BAY; STATE-OF-THE-ART ALGORITHMS; TEXTURAL INFORMATION; TEXTURE FEATURES;

ARTS COMPUTING; ELECTROMAGNETISM; FEATURE EXTRACTION; IMAGE CLASSIFICATION; IMAGING SYSTEMS; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); POLARIMETERS; POLAROGRAPHIC ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; SYNTHETIC APERTURE RADAR;

COVARIANCE MATRIX;

EID: 79952692826     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Pottier, E. and J. S. Lee, "Unsupervised classification scheme of POLSAR images based on the complex Wishart distribution and the H/A/α-Polarimetric decomposition theorem," Proc. of the 3rd EUSAR 2000 Conf., May 2000.
2. Van Zyl, J. J., "Unsupervised classification of scattering mechanisms using radar polarimetry data," IEEE Trans. Geosci. Remote Sensing, Vol. 27, 36-45, Jan. 1989.
3. Lee, J. S., M. R. Grunes, T. Ainsworth, L.-J. Du, D. Schuler, and S. R. Cloude, "Unsupervised classification using polarimetric decomposition and the complex Wishart classifier," IEEE Trans. Geosci. Remote Sens., Vol. 37, No. 5, 2249-2257, Sep. 1999.
4. Wu, Y., K. Ji, W. Yu, and Y. Su, "Region-based classification of polarimetric SAR images using Wishart MRF," IEEE Geosci. Rem. Sens. Lett., Vol. 5, No. 4, 668-672, 2008.
5. Tan, C. P., K. S. Lim, and H. T. Ewe, "Image processing in polarimetric SAR images using a hybrid entropy decomposition and maximum likelihood (EDML)," Proc. Int. Symposium on Image and Signal Processing and Analysis (ISPA), 418-422, Sep. 2007.
6. Ye, Z. and C.-C. Lu, "Wavelet-based unsupervised SAR image segmentation using hidden markov tree models," Proc. of the 16th International Conference on Pattern Recognition (ICPR'02), Vol. 2, 20729, 2002.
7. Kong, J. A., A. A. Swartz, H. A. Yueh, L. M. Novak, and R. T. Shin, "Identification of terrain cover using the optimum polarimetric classifier," Journal of Electromagnetic Waves and Applications, Vol. 2, No. 2, 171-194, 1988.
8. Cloude, S. R. and E. Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," IEEE Trans. Geosci. Remote Sensing, Vol. 35, 68-78, Jan. 1997. (Pubitemid 127771044)
9. Khan, K. U., J. Yang, and W. Zhang, "Unsupervised classification of polarimetric SAR images by EM algorithm," IEICE Transactions on Communications, Vol. 90, No. 12, 3632-3642, 2007.
10. Zhang, Y. D., L.-N. Wu, and G. Wei, "A new classifier for polarimetric SAR images," Progress In Electromagnetics Research, PIER 94, 83-104, 2009.
11. Yang, S. Y., M. Wang, and L. C. Jiao, "Radar target recognition using contourlet packet transform and neural network approach," Signal Processing, Vol. 89, No. 4, 394-409, 2009.
12. Kiranyaz, S., T. Ince, A. Yildirim, and M. Gabbouj, "Fractional particle swarm optimization in multi-dimensional search space," IEEE Transactions on Systems, Man, and Cybernetics - Part B, Vol. 40, No. 2, 298-319, 2010.
13. Lee, J. S., M. R. Grunes, and R. Kwok, "Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution," Int. J. Rem. Sens., Vol. 15, No. 11, 2299-2311, 1994.
14. Clausi, D. A., "An analysis of co-occurrance texture statistics as a function of grey level quantization," Canadian J. Remote Sensing, Vol. 28, No. 1, 45-62, 2002.
15. Poggio, T. and F. Girosi, "A theory of networks for approximation and learning," A. I. Memo, No. 1140, M.I.T. A.I Lab, 1989.
16. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, USA, Jun. 1998.
17. Chauvin, Y. and D. E. Rumelhart, Back Propagation: Theory, Architectures, and Applications, Lawrence Erlbaum Associates Publishers, UK, 1995.
18. Riedmiller, M. and H. Braun, "A direct adaptive method for faster backpropagation learning: The RPROP algorithm," Proc. of the IEEE International Conference on Neural Networks, 586-591, 1993.
19. Kiranyaz, S., T. Ince, A. Yildirim, and M. Gabbouj, "Evolutionary artificial neural networks by multi-dimensional particle swarm optimization," Neural Networks, Vol. 22, No. 10, 1448-1462, 2009.
20. U.S. Geological Survey Images, [Online], Available: http://terraserver- usa.com.
21. Lee, J. S., M. R. Grunes, and G. de Grandi, "Polarimetric SAR speckle filtering and its implications for classification," IEEE Trans. Geosci. Remote Sensing, Vol. 37, No. 5, 2363-2373, 1999.
22. Fukuda, S. and H. Hirosawa, "A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images," IEEE Trans. on Geoscience and Remote Sensing, Vol. 37, No. 5, 2282-2286, 1999. (Pubitemid 30532656)