참고문헌
- Carl Staelin, 2003, Parameter selection for SVMs, Hewlett-Packard Company.
- Chang, Q., Chen, Q., Wang, X., 2005, Scaling gaussian RBF kernel width to improve SVM classification, ICNN&B 05' International Conference, Vol. 1.
- Chi, M., Feng, R., Bruzzone, L., 2008, Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem, Advances in Space Research, Vol. 41, No. 11, pp. 1793-1799. https://doi.org/10.1016/j.asr.2008.02.012
- Choi, Jae Wan, Byun, Young Gi, Kim, Yong Il, Yu, Ki Yun, 2006, Support vector machine classification of hyperspectral image using spectral similarity kernel, The Korean Society for Spatial Information system, Vol. 14, No. 4, pp. 71-77.
- Foody, G.M., Mathur, A., 2004, A relative evaluation of multi-class image classification by SVMs, IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 6, pp. 1335-1343. https://doi.org/10.1109/TGRS.2004.827257
- Han, Seung Hee, 2010, Spatial Information engineering, Goomi Book.
- Kang, Nam Yi, Pak, Jung Gi, Cho, Gi Sung, Yeu, Yeon, 2012, An analysis of land cover classification methods using IKONOS satellite image, The Korean Society for Spatial Information system, Vol. 20, No. 3, pp. 65-70.
- Kim, Gi Sung, 2003, Classification using support vector machine, Thesis, Inha University.
- Kim, Hyo Mi, 2002, Classification of multi-class micro array gene expression data using SVM, Thesis, Yonsei University.
- Lee, Chang Seok, 2011, Adult image detection based on the skin region distribution using SVM, Thesis, Hanbat National University.
- Lee, Min Hoon, 2006, Study on classification of object and non-object images based on the color and texture significance, Thesis, Kumoh national Institute of Technology.
- Muller, K., Mika, S., Ratisch, G., Tsuda, K., Scholkopf, B., 2001, An introduction to kernel-based learning algorithms, IEEE Transactions On Neural Networks, Vol. 12, NO. 2.
- Prasad, S.V.S., Satya Savitri, T., Murali Krishna, I.V., 2011, Classification of multispectral satellite images using clustering with SVM classifier, International Journal of Computer Applications, Vol.35, No. 5, pp. 32-44. https://doi.org/10.5120/4399-6107
- Richards, John A., 1994, Remote sensing digital image analysis : An introduction, second, Revised and Enlarged Edition, pp.229-262, Springer-Verlag.
- Scholkopf, Bernhard., Smola, Alexander J., 2002, Leaning with kernels, The MIT Press, London.
- Schowengerdt, R., 1983, Techniques of image processing and classification in remote sensing, 1st Ed, pp. 1-58, Academic Press.
- Vapnik, Vladimir N., 1995, The nature of statical learning theory, Springer-Verlag, NewYork.
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