Support Vector Machine (SVM) Classification: Comparison of Linkage Techniques Using a Clustering-Based Method for Training Data Selection

References

• Bruzzone, L., Chi, M., and M. Marconcini, 2006, "A Novel Transductive SVM for Semisupervised Classification of RemoteSensing Images," IEEE Transactions on Geoscience and Remote Sensing , 44(11):3363-3373.
   Google Scholar
• Burges, C. J. C., 1998, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery , 2:121-167.
   Google Scholar
• Campbell, J. B., 1996, Introduction to Remote Sensing (2rd ed.), London, UK: Taylor and Francis, 622 p.
   Google Scholar
• Chang, C. C. and C. J. Lin, 2001, LIBSVM: A Library for Support Vector Machines, 2001 [ http://www.csie.ntu.edu.tw/~cjlin/libsvm
   Google Scholar
• Diner, D. J., Martonchik, J. V., Borel, C., Gerstl, S. A. W., Gordon, H. R., Knyazikhin, Y., Myneni, R., Pinty, B., and M. M. Verstraete, 1999, "Multi-angle Imaging Spectro-Radiometer Level 2 Surface Retrieval Algorithm Theoretical Basis Document," December, 1999, Pasadena, CA: California Institute of Technology, Jet Propulsion Laboratory [ http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/atbdmisr10.pdf
   Google Scholar
• Foody, G. M. and A. Mathur, 2004, "Toward Intelligent Training of Supervised Image Classifications: Directing Training Data Acquisition for SVM Classification," Remote Sensing of Environment , 93(12):107-117.
   Google Scholar
• Gan, G., Ma, C., and J. Wu, 2007. Data Clustering: Theory, Algorithms, and Applications , Philadelphia, PA/Alexandria, VA: SIAM/ASA, ASA-SIAM Series on Statistics and Applied Probability, 466 p.
   Google Scholar
• Gibbens, R. P., McNeely, R. P., Havstad, K. M., Beck, R. F., and B. Nolen, 2005, "Vegetation Changes in the Jornada Basin from 1858 to 1998," Journal of Arid Environments , 61(4):651-668.
   Google Scholar
• Havstad, K. M., Kustas, W. P., Rango, A., Ritchie, J. C., and T. J. Schmugge, 2000, "Jornada Experimental Range: A Unique Arid Land Location for Experiments to Validate Satellite Systems," Remote Sensing of Environment , 74(1):13-25.
   Google Scholar
• Hill, H. and P. Lewicki, 2006, Statistics: Methods and Applications: A Comprehensive Reference for Science, Industry, and Data Mining , Tulsa, OK: StatSoft, Inc., 120 p.
   Google Scholar
• Huang, C., Davis, L. S., and J. R. G. Townshend, 2002, "An Assessment of Support Vector Machines for Land Cover Classification," International Journal of Remote Sensing , 23(4):725-749.
   Google Scholar
• Jensen, J. R., 2005, Introductory Digital Image Processing: A Remote Sensing perspective (3rd ed.), Upper Saddle River, NJ: Prentice-Hall, 526 p.
   Google Scholar
• Koggalage, R. and S. Halgamuge, 2004, "Reducing the Number of Training Samples for Fast Support Vector Machine Classification," Neural Information Processing Letters & Reviews , 2(3):57-65.
   Google Scholar
• Lin, C. and S. Wang, 2002, "Fuzzy Support Vector Machines," IEEE Transactions, Neural Networks , 13(2):464-471.
   Google Scholar
• Mahalanobis, P. C., 1936, "On the Generalised Distance in Statistics," Proceedings of the National Institute of Sciences of India , 2(1):49-55.
   Google Scholar
• Richards, J. A. and X. Jia, 1999, Remote Sensing Digital Image Analysis: An Introduction (3rd ed.), Berlin, Germany: Springer, 363 p.
   Google Scholar
• Shin, H. J. and S. Z. Cho, 2003, "Fast Pattern Selection for Support Vector Classifiers," in Proceedings of 7th Pacific—Asia Conference on Knowledge Discovery and Data Mining , Seoul, Korea, April 30-May 2 2003, 376-387.
   Google Scholar
• Su, L., 2009, "Optimizing Support Vector Machine Learning for Semi-arid Vegetation Mapping by Using Clustering Analysis," ISPRS Journal of Photogrammetry and Remote Sensing , 64(4):407-413.
   Google Scholar
• Su, L., Chopping, M. J., Rango, A., Martonchik, J. V., and D. P. C. Peters, 2007, "Support Vector Machines for Recognition of Semiarid Vegetation Types Using MISR Multi-angle imagery," Remote Sensing of Environment , 107(12):299-311.
   Google Scholar
• Su, L., Huang, Y., Chopping, M. J., Rango, A., and J. V. Martonchik, 2009, "An Empirical Study on the Utility of Topographic Parameters and BRDF Model Parameters for Mapping Vegetation in a Semi-arid Region with MISR imagery," International Journal of Remote Sensing , 30(13):3463-3483.
   Google Scholar
• Tax, D. M. J. and R. P. W. Duin, 2001, "Uniform Object Generation for Optimizing One-Class Classifiers," Journal of Machine Learning Research , 2:155-173.
   Google Scholar
• Vapnik, V., 1995, Statistical Learning Theory , New York, NY: Springer-Verlag, 736 p.
   Google Scholar
• Zhang, W. and I. King, 2002, "Locating Support Vectors via β-skeleton Technique," in Proceedings of the 9th International Conference on Neural Information Processing (ICONIP), Singapore, November 18-22, 2002, 1423-1427.
   Google Scholar