Fusion of support vector machines for classification of multisensor data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 12, 2007, Pages 3858-3866

  Waske, Björn ^a,b   Benediktsson, Jón Atli ^a,c  

^a IEEE   (Germany)

^b UNIVERSITY OF BONN   (Germany)

^c UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Data fusion; Multisensor imagery; Multispectral data; Support vector machines (SVM); Synthetic aperture radar (SAR) data

Indexed keywords

MULTISENSOR IMAGERY; MULTISPECTRAL DATA; SYNTHETIC APERTURE RADAR (SAR) DATA;

DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION; RADAR IMAGING; SUPPORT VECTOR MACHINES; SYNTHETIC APERTURE RADAR;

SENSOR DATA FUSION;

EID: 36349007145     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.898446     Document Type: Conference Paper

References (43)

1. J. A. Benediktsson and I. Kanellopoulos, "Classification of multisource and hyperspectral data based on decision fusion," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1367-1377, May 1999.
2. X. Blaes, L. Vanhalle, and P. Defourny, "Efficiency of crop identification based on optical and SAR image time series," Remote Sens. Environ., vol. 96, no. 3/4, pp. 352-365, Jun. 2005.
3. G. Chust, D. Ducrot, and J. L. Pretus, "Land cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediteranean cultural landscape," Int. J. Remote Sens., vol. 25, no. 17, pp. 3513-3528, 2004.
4. D. B. Michelson, B. M. Liljeberg, and P. Pilesjo, "Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data," Remote Sem. Environ., vol. 7.1, no. 1, pp. 1-15, Jan. 2000.
5. J. A. Benediktsson, P. H. Swain, and O. K. Ersoy, "Neural network approaches versus statistical methods in classification of multisource remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 28, no. 4, pp. 540-552, Jul. 1990.
6. T. Lee, J. A. Richards, and P. H. Swain, "Probabilistic and evidential approaches for multisource data analysis," IEEE Trans. Geosci. Remote Sens., vol. GRS-25, no. 3, pp. 283-293, May 1987.
7. S. B. Serpico and F. Roli, "Classification of multisensor remote-sensing images by structured neural networks," IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 562-578, May 1995.
8. M. A. Friedl and C E. Brodley, "Decision tree classification of land cover from remotely sensed data," Remote Sens. Environ., vol. 61, no. 3, pp. 399-409, Sep. 1997.
9. M. Pal and P. M. Mather, "An assessment of the effectiveness of decision tree methods for land cover classifications," Remote Sens. Environ., vol. 86, no. 4, pp. 554-565, Aug. 2003.
10. C. Huang, L. S. Davis, and J. R. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote Sens., vol. 23, no. 4, pp. 725-749, Feb. 2002.
11. G. M. Foody and A. Mathur, "The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM," Remote Sens. Environ., vol. 103, no. 2, pp. 179-189, Jul. 2006.
12. T. Kavzoglu and P. M. Mather, "Pruning artificial neural networks: An example using land cover classification of multi-sensor images," Int. J. Remote Sens., vol. 20, no. 14, pp. 2787-2803, Sep. 1999.
13. M. A. Friedl, C E. Brodley, and A. H. Strahler, "Maximizing land cover classification accuracies produced by decision trees at continental to global scales," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 969-977, Mar. 1999.
14. M. Simard, S. S. Saatchi, and G. de Grandi, "The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2310-2321, Sep. 2000.
15. B. M. Steele, "Combining multiple classifiers: An application using spatial and remotely sensed information for land cover type mapping," Remote Sens. Environ., vol. 74, no. 3, pp. 545-556, Dec. 2000.
16. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Decision fusion for the classification of urban remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2828-2838, Oct. 2006.
17. J. A. Benediktsson and P. H. Swain, "Consensus theoretic classification methods," IEEE Trans. Syst., Man, Cybern., vol. 22, no. 4, pp. 688-704, Jul./Aug. 1992.
18. J. A. Benediktsson, J. R. Sveinsson, and P. H. Swain, "Hybrid consensus theoretic classification," IEEE Trans. Geosci. Remote Sem., vol. 35, no. 4, pp. 833-843, Jul. 1997.
19. G. M. Foody and A. Mathur, "A relative evaluation of multiclass image classification, of support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, pp. 1335-1343, Jun. 2004.
20. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
21. M. Pal and P. M. Mather, "Support vector machines for classification in remote sensing," Int. J. Remote Sens., vol. 26, no. 5, pp. 1007-1011, Mar. 2005.
22. M. Pal and P. M. Mather, "Some issues in the classification of DAIS hyperspectral data," Int. J. Remote Sens., vol. 27, no. 14, pp. 2895-2916, Jul. 2006.
23. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
24. X. Song, G. Fan, and M. Rao, "Automatic CRP mapping using nonparametric machine learning approaches," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 888-897, Apr. 2005.
25. G. H. Halldorsson, J. A. Benediktsson, and J. R. Sveinsson, "Support vector machines in multisource classification," in Proc. IGARSS, 2003, vol. 3, pp. 2054-2056.
26. G. Camps-Vails, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
27. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "A combined support vector machines classification based on decision fusion," in Proc. IGARSS, 2006, pp. 2494-2497.
28. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Min. Knowl, Discov., vol. 2, no. 2, pp. 121-167, Jun. 1998.
29. B. Schölkopf and A. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
30. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, "Choosing multiple parameters for support vector machines," Mach. Learn., vol. 46, no. 1-3, pp. 131-159, Jan. 2001.
31. K.-M. Chung, W.-C. Kao, C.-L. Sun, L.-L. Wang, and C.-J. Lin, "Radius margin bounds for support vector machines with the RBF kernel," Neural Comput., vol. 15, no. 11, pp. 2643-2681, Nov. 2003.
32. J. H. Lee and C. J. Lin, "Automatic model selection for support vector machines, Technical report," Dept. Comput. Sci. and Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan, R.O.C., 2000. [Online]. Available: http://www.csie. ntu.edu. tw/~cjlin/looms/
33. C.-W. Hsu and C.-J. Lin, "A comparison of methods for multi-class support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
34. D. J. Sebald and J. A. Bucklew, "Support vector machines and the multiple hypothesis test problem," IEEE Trans. Signal Process., vol. 49, no. 11, pp. 2865-2872, Nov. 2001.
35. H. Laur, P. Bally, P. Meadows, J. Sanchez, B. Schaettler, E. Lopinto, and D. Esteban, "Derivation of the backscattering coefficient σ0 in ESA ERS SAR PRI products," ESA, Noordjiwk, The Netherlands, ESA Document ES-TN-RE-PM-HL09, 2002, no. 2, Rev. 5d.
36. D. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ: Wiley, 2003.
37. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction. New York: Springer-Verlag, 2003.
38. J. R. Quinlan, "Induction of decision trees," Mach. Learn., vol. 1, no. 1, pp. 81-106, 1986.
39. J. R. Quinlan, C4.5: Programs for Machine Learning. Los Altos, CA: Morgan Kaufmann, 1993.
40. G. J. Briem, J. A. Benediktsson, and J. R. Sveinsson, "Multiple classifiers applied to multisource remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2291-2299, Oct. 2002.
41. B. Waske, S. Schiefer, and M. Braun, "Random feature selection for a decision tree classification of multi-temporal SAR data," in Proc. IGARSS, 2006, pp. 168-171.
42. Y. Freund and R. E. Schapire, "Experiments with a new boosting algorithm," in Proc. 13th Int. Conf. Mack Learn., 1996, pp. 148-156.
43. R. G. Congalton and K. Green, Assessing the Accuracy of Remote Sensed Data: Principles and Practices. Boca Raton, FL: Lewis Publishers, 1999.