Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers

Photogrammetric Engineering and Remote Sensing

Volumn 74, Issue 2, 2008, Pages 239-246

  Watanachaturaporn, Pakorn ^a   Arora, Manoj K ^b   Varshney, Pramod K ^c  

^a KING MONGKUT'S INSTITUTE OF TECHNOLOGY LADKRABANG   (Thailand)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c Syracuse University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; GEOLOGY; GEOPHYSICS; NEURAL NETWORKS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

DIGITAL ELEVATION MODEL (DEM); MULTISOURCE CLASSIFICATION; THEMATIC INFORMATION;

IMAGE CLASSIFICATION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DIGITAL ELEVATION MODEL; IMAGE CLASSIFICATION; LAND CLASSIFICATION; LAND COVER; MACHINERY; NDVI; REMOTE SENSING;

ASIA; EURASIA; HIMALAYAS;

EID: 38949149377     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.74.2.239     Document Type: Article

References (50)

1. Arora, M.K., and S. Mathur, 2001. Multi-source classification using artificial neural network in rugged terrain, GeoCarto International, 16(3):37-44.
2. Atkinson, P.M., and A.R.L. Tatnall, 1997. Neural network in remote sensing, International Journal of Remote Sensing, 18(4):699-709.
3. Benediktsson, J.A., and J.R. Sveinsson, 2003. Multisource remote sensing data classification based on consensus and pruning, IEEE Transactions on Geoscience and Remote Sensing, 41(4):932-936.
4. Bruzzone, L., and D.F. Prieto, 1999. A technique for the selection of kernel function parameters in RBF neural networks for classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 37:1179-1184.
5. Bruzzone, L., D.F. Prieto, and S.B. Serpico, 1999. A neural statistical approach to multitemporal and multisensor and multisource remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing, 37(3):1350-1359.
6. Bruzzone, L., M. Chi, and M. Marconcini, 2006. A novel transductive SVM for semisupervised classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 44(11):3363-3373.
7. Congalton, R.G., and K. Green, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.
8. Cristianini, N., and J. Shawe-Taylor, 2000. An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods, University Press, Cambridge, U.K., New York, Cambridge.
9. Debeir, O., P. Latinne, and I.V.D. Steen, 2001. Remote sensing classification of spectral, spatial and contextual data using multiple classifier systems, Image Analysis and Stereology, 20(Supplement 1):584-589.
10. Fitzgerald, R.W., and B.G. Lees, 1994. Assessing the classification accuracy of multisource remote sensing data, Remote Sensing of Environment, 47(3):362-368.
11. Fletcher, R., 1987. Practical Methods of Optimization, Second edition, Chichester, Wiley, New York.
12. Foody, G.M., 1995. Land cover classification by an artificial neural network with ancillary information, International Journal of Geographical Information System, 9(5):527-542.
13. Foody, G.M., and M.K. Arora, 1997. An evaluation of some factors affecting the accuracy of an artificial neural network, International Journal of Remote Sensing, 18(4):789-810.
14. Foody, G.M., 2004. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy, Photogrammetric Engineering & Remote Sensing, 70(5):627-633
15. Foody, G.M., and A. Mathur, 2004a. A relative evaluation of multiclass image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(6):1335-1343.
16. Foody, G.M., and A. Mathur, 2004b. Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification, Remote Sensing of Environment, 93(1-2):107-117.
17. Gualtieri, J.A., S.R. Chettri, R.F. Cromp, and L.F. Johnson, 1999. Support vector machine classifiers as applied to AVIRIS data, The Summaries of the Eighth JPL Airborne Earth Science Workshop.
18. Halldorsson, G.H., J.A. Benediktsson, and J.R. Sveinsson, 2003. Support vector machines in multisource classification, Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS '03, IEEE International.
19. Hansen, M., R. Dubayah, and R. DeFries, 1996. Classification trees: An alternative to traditional land-cover classifiers, International Journal of Remote Sensing, 17:1075-1081.
20. Haykin, S.S., 1999. Neural Networks: A Comprehensive Foundation, Second edition, Upper Saddle River, New Jersey, Prentice Hall.
21. Huang, C., L.S. Davis, and J.R. Townshend, 2002. An assessment of support vector machines for land-cover classification, International Journal of Remote Sensing, 23(4):725-749.
22. Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Second edition, Upper Saddle River, New Jersey, Prentice Hall.
23. Key, J.R., J.A. Maslanik, and R.G. Barry, 1989. Cloud classification from satellite data using a fuzzy sets algorithm: A polar example, International Journal of Remote Sensing, 10:1823-1842.
24. Light, W.A., 1992. Ridge functions, sigmoidal functions and neural networks, Approximation Theory VII (E.W. Cheney, C.K. Chui, and L.L. Schumaker, editors), Boston, Academic Press, pp. 163-206.
25. Mangasarian, O.L., and D.R. Musicant, 2001. Lagrangian support vector machines, Journal of Machine Learning Research, 1:161-177.
26. Mather, P.M., 1999. Computer Processing of Remotely-sensed Images: An Introduction, Second edition, Chichester, New York, Wiley.
27. Melgani, F., and L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8):1778-1790.
28. Pal, M., and P.M. Mather, 2005. Support vector machines for classification in remote sensing, International Journal of Remote Sensing, 26(5):1007-1011.
29. Paola, J.D., and R.A. Schowengerdt, 1995. A review and analysis of backpropagation neural networks for classification of remote sensed multi-spectral imagery, International Journal of Remote Sensing, 16:3033-3058.
30. Paola, J.D., and R.A. Schowengerdt, 1997. The effect of neural network structure on a multispectral land-use/land-cover classification, Photogrammetric Engineering & Remote Sensing, 63:535-544.
31. Peddle, D.R., G.M. Foody, A. Zhang, S.E. Franklin, and E.F. LeDrew, 1994. Multisource image classification II: An empirical comparison of evidential reasoning and neural network approaches, Canadian Journal of Remote Sensing, 20:396-407.
32. Pohl, C., and J.L. van Genderen, 1998. Multisensor image fusion in remote sensing: Concepts, methods and applications, International Journal of Remote Sensing, 19(5):823-854.
33. Powell, M.J.D., 1985. Radial basis functions for multivariable interpolation: A review, Proceedings of the IMA Conference on Algorithms for the Approximation of the Functions and Data, RMCS, Shrivenham, England.
34. Powell, M.J.D., 1988. Radial basis function approximations to polynomials, Proceedings of the Numerical Analysis 1987, Dundee, U.K.
35. Powell, M.J.D., 1992. The theory of radial basis function approximation in 1990, Advances in Numerical Analysis, Volume II: Wavelets, Subdivision Algorithms, and Radial Basis Functions (W. Light, editor), Oxford, Oxford Science Publications, pp. 105-210.
36. Quinlan, J.R., 1993. C4.5: Programs for Machine Learning, San Mateo, California, Morgan Kaufmann Publishers.
37. Rao, R.M., and M.K. Arora, 2004. Overview of image processing, Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data (P.K. Varshney and M.K. Arora, editors), Springer-Verlag, pp. 51-85.
38. Richards, J.A., and X. Jia, 1999. Remote Sensing Digital Image Analysis: An Introduction, Third edition, Berlin, New York, Springer.
39. Rosenfield, G.H., and K. Fitzpatrick-Lins, 1986. A measure of agreement as a measure of thematic classification accuracy, Photogrammetric Engineering & Remote Sensing, 52(2):223-227.
40. Safavian, S.R., and D. Landgrebe, 1991. A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics, 21:660-674.
41. Saha, A.K., M.K. Arora, E. Csaplovics, and R.P. Gupta, 2005. Land cover classification using IRS LISS III image and DEM in a rugged terrain: A case study in Himalayas, GeoCarto International.
42. th International Joint Conference on Artificial Intelligence.
43. Simone, G., A. Farina, F.C. Morabito, S.B. Serpico, and L. Bruzzone, 2002. Image fusion techniques for remote sensing applications, Information Fusion, 3(1):3-15.
44. Srinivasan, A., and J.A. Richards, 1990. Knowledge-based techniques for multi-source classification, International Journal of Remote Sensing, 11(3):505-525.
45. Tso, B., and P.M. Mather, 2001. Classification Methods for Remotely Sensed Data, London, New York, Taylor & Francis.
46. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory, New York, Springer.
47. Watanachaturaporn, P., and M.K. Arora, 2004. Support vector machines for classification of multi- and hyperspectral data Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data (P.K. Varshney and M.K. Arora, editors), Springer-Verlag, pp. 237-255.
48. Watanachaturaporn, P., M.K. Arora, and P.K. Varshney, 2004. Evaluation of factors affecting support vector machines for hyperspectral classification, Proceedings of the ASPRS 2004 Annual Conference, 23-28 May, Denver, Colorado, unpaginated CD-ROM.
49. Watanachaturaporn, P., M.K. Arora, and P.K. Varshney, 2005. Hyperspectral image classification using support vector machines: A comparison with decision tree and neural network classifiers, Proceedings of the ASPRS 2005 Annual Conference, Baltimore, Maryland, unpaginated CD-ROM.
50. Wolpert, D.H., 1992. Stacked generalization, Neural Networks, 5(2):241-260.