The International Archives oj the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
REFERENCES 
Congalton, R.G. and Mead, R.A., 1983, A quantitative method 
to test for consistency and correctness in photointerpretation, 
Photogrammetric Engineering and Remote Sensing, 49: 69-74. 
Cristianini, N. and Scholkopf, B., 2002, Support vector 
machines and kernel methods - the new generation of learning 
machines, Ai Magazine, 23: 31-41. 
Eric A. Whitsel, Kathryn M. Rose, Joy L. Wood et al. 2004 
Accuracy and Repeatability of Commercial Geocoding. 
American Journal of Epidemiology, 160(10): 1023-1029. 
Foody, G.M., Mathur, A., Sanchez-Hemandez, C. and Boyd, D., 
2006, Training set size requirements for the classification of a 
specific class, Remote Sensing of Environment, 104: 1-14. 
Guo, Q., Kelly, M., Gong, P. and Liu, D., 2007, An object- 
based classification approach in mapping tree mortality using 
high spatial resolution imagery, GIScience & Remote Sensing, 
44: 24-47. 
Huang, C., Davis, L.S. and Townshend, J.R.G., 2002, An 
assessment of support vector machines for land cover 
classification, International Journal of Remote Sensing, 23: 
725-749. 
Martin, L.R.G. and Howarth, P.J., 1989, Change-detection 
accuracy assessment using spot multispectral imagery of the 
rural urban fringe, Remote Sensing of Environment, 30: 55-66. 
Mumby, P.J. and Edwards, A.J., 2002, Mapping marine 
environments with ikonos imagery: Enhanced spatial resolution 
can deliver greater thematic accuracy, Remote Sensing of 
Environment, 82: 248-257. 
Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and 
Williamson, R.C., 1999, Estimation the support of a high 
dimensional distribution, Technical Report MSR-TR-99-87, 
Microsoft Research. 
Vapnik, V., 1995, The nature of statistical learning theory, New 
York: Springer-Verlag. 
Xia, L., 1996, A method to improve classification with shape 
information, International Journal of Remote Sensing, 17: 
1473-1481.