Contextual PolSAR image classification using fractal dimension and support vector machines

European Journal of Remote Sensing

Volumn 46, Issue 1, 2013, Pages 317-332

  Aghababaee, Hossein ^a   Amini, Jalal ^a   Tzeng, Yu Chang ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b NATIONAL UNITED UNIVERSITY   (Taiwan)

Author keywords

Classification; Fractal dimension; PolSAR image; Support vector machines; Wavelet multi resolution

Indexed keywords

FRACTAL DIMENSION; FUZZY LOGIC; IMAGE CLASSIFICATION; SUPPORT VECTOR MACHINES; SYNTHETIC APERTURE RADAR; VECTORS;

ADAPTIVE WINDOWS; CLASSIFICATION SCHEME; CONTEXTUAL INFORMATION; FRACTIONAL BROWNIAN MOTION; NONLINEAR CLASSIFIERS; POLARIMETRIC SYNTHETIC APERTURE RADARS; POLSAR IMAGE; WAVELET MULTI-RESOLUTION ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 84876949203     PISSN: None     EISSN: 22797254     Source Type: Journal    
DOI: 10.5721/EuJRS20134618     Document Type: Article

References (31)

1. Aghababaee H., Tzeng Y.C., Amini J. (2012). Swarm intelligence and fractals in dualpol synthetic aperture radar image change detection. SPIE Journal of Applied Remote Sensing, vol. 6 (1): 063596. doi: http://dx.doi.org/10.1117/1.JRS.6.063596.
2. Beaulieu J.M., Touzi R. (2004). Segmentation of textured polarimetric SAR scenes by likelihood approximation. IEEE Transactions on Geoscience and Remote Sensing, 42 (10): 2063-2072. doi: http://dx.doi.org/10.1109/TGRS.2004.835302.
3. Betti A., Barni M., Mecocci A. (1997). Using a wavelet-based fractal feature to improve texture discrimination on SAR images. IEEE International Conference on Image Processing, 1: 251-254. doi: http://dx.doi.org/10.1109/ICIP.1997.647752.
4. Cloude S.R., Pottier E. (1997). An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transaction on Geoscience and Remote Sensing, 35 (1): 68-78. doi: http://dx.doi.org/10.1109/36.551935.
5. Goodman J. (1976). Some fundamental properties of speckle. Journal of Optical Society of America, 66 (11): 1145-1150. doi: http://dx.doi.org/10.1364/JOSA.66.001145.
6. Goumehei E., Tolpekin V.A. (2011). Contextual image classification with support vector machines. Proceeing of 1th International conference on sensor and models in photogrammetry and remote sensing (SMPR), Tehran, Iran.
7. Henderson F.M., Lewis A.J. (1998). Principles and Applications of Imaging Radar. Manual of Remote Sensing. Third Edition, Volume 2, ASPRS, John Wiley and Sons Inc., Toronto
8. Hsu C.W., Chang C.C., Lin C.J. (2003). A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University.
9. Huang C., Davis L.S., Townshend J.R.G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23 (4): 725-749. doi: http://dx.doi.org/10.1080/01431160110040323.
10. Kourgli A., Oukil Y., Hirche A., Ouarzeddine M. (2011). Polarimetric SAR images segmentation incorporating texture features. IEEE 17th International Conference on Digital Signal Processing (DSP), pp 1-5. doi: http://dx.doi.org/10.1109/ICDSP.2011.6004979.
11. Liu M., Zhang H., Wang C. (2011). Applying the log-cumulants of texture parameter to fully polarimetric SAR classification using Support Vector Machines Classifier. IEEE CIE International Conference on Radar, 1: 728-731. doi: http://dx.doi.org/10.1109/CIE- Radar.2011.6159644.
12. Lopes A., Touzi R., Nezry E. (1990). Adaptive speckle filters and Scene heterogeneity. IEEE Transaction on Geoscience and Remote Sensing, 28 (6): 992-1000. doi: http:// dx.doi.org/10.1109/36.62623.
13. Mandelbrot B.B., Van Ness J.W. (1968). Fractional Brownian motions, fractional noises and applications. SIAM Review, 10 (4): 422-437. doi: http://dx.doi.org/10.1137/1010093
14. Mcdonald M.K., Varadan V., Leung H. (2002). Chaotic behavior and non-linear prediction of airborne radar sea clutter data. IEEE International conference of Radar, pp. 331-337. doi: http://dx.doi.org/10.1109/NRC.2002.999740.
15. Melgani F., Bruzzone L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42 (8): 1778-1790. doi: http://dx.doi.org/10.1109/TGRS.2004.831865.
16. Mountrakis G., Jungho I., Ogole C. (2010). Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3): 247-259. doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001.
17. Novianto S., Guimaraes L., Suzuki Y., Maeda J., Anh V.V. (1999). Multi-windowed Approach to the optimum Estimation of the Local Fractal Dimension for Natural Image Segmentation. IEEE international conference on Image Processing, 3: 222-226. doi: http://dx.doi.org/10.1109/ICIP.1999.817105.
18. Pal M., Mathur P.M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26 (5): 1007-1011. doi: http://dx.doi.org/10.1 080/01431160512331314083.
19. Pant T., Singh D., Srivastava T. (2010). Advanced fractal approach for unsupervised classification of SAR images. Elsevier Advances in Space Research, 45: 1338-1349. doi: http://dx.doi.org/10.1016/j.asr.2010.01.008.
20. Parra C., Iftekharuddin K., Rendon D. (2003). Wavelet Based Estimation of the Fractal Dimension in fBm Images. IEEE International conference on neural engineering, Capri Island, Italy, pp 533-536. doi: http://dx.doi.org/10.1109/CNE.2003.1196881.
21. Rodionova N.V. (2007). A combined use of decomposition and texture for terrain classification of fully polarimetric SAR images. Workshop of POLinSAR, 22-26 January, ESAESRIN, Frascati, Italy.
22. Samadzadegan F., Ferdosi E. (2012). Classification of Polarimetric SAR Images Based on Optimum SVMs Classifier Using Bees Algorithm. International Conference on Intelligent Computational Systems (ICICS), Dubai. UAE.
23. Touzi R. (2007). Target scattering decomposition in terms of Roll-Invariant target parameters. IEEE transaction on Geoscience and Remote Sensing, 45 (1): 73-84. doi: http://dx.doi.org/10.1109/TGRS.2006.886176.
24. Tso B., Mathur P.M. (2009). Classification methods for remotely sensed data. Second edition. CRC Press, Boca Raton, FL. doi: http://dx.doi.org/10.1201/9781420090741.
25. Tzeng Y.C, Chiu S.H., Chen K.S. (2007). Change Detections from SAR Images for Damage Estimation Based on a Spatial Chaotic Model. IEEE International conference on Geoscience and Remote Sensing Symposium, Barcelona, Spain, pp. 1926-1930. doi: http://dx.doi.org/10.1109/IGARSS.2007.4423203.
26. Van Zyl J.J. (1989). Unsupervised classification of scattering mechanisms using radar polarimetry data. IEEE Transaction on Geoscience and Remote Sensing, 27 (1): 36-45. doi: http://dx.doi.org/10.1109/APSAR.2009.5374123.
27. Vapnik V. (1998). Statistical learning theory. New York: John Wiley and Sons Inc.
28. Wu D., Linders J. (1999). A new texture approach to discrimination of forest clear cut, canopy, and burned area using airborne C-band SAR. IEEE Transaction on Geoscience and Remote Sensing, 37 (1): 555-563. doi: http://dx.doi.org/10.1109/36.739113.
29. Yu P., Qin A.K. (2012). Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty. IEEE Transaction on Geoscience and Remote Sensing, 50 (4): 1302-1317. doi: 10.1109/TGRS.2011.2164085.
30. Zhang L., Zou B., Zhang J., Zhang Y. (2010). Classification of polarimetric SAR image based on support vector machine using multiple-component scattering model and texture features. Eurasip Journal on Advaces in Signal processing.
31. Hindawi Zhou G., Cui Y., Chen Y., Yin J., Yang J., Su Y. (2010). Pol-SAR images classification using texture features and the complex Wishart distribution. IEEE Radar Conference, pp. 491-494. doi: http://dx.doi.org/10.1109/RADAR.2010.5494572.