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http://dx.doi.org/10.7319/kogsis.2013.21.2.019

A Comparative Study on Suitable SVM Kernel Function of Land Cover Classification Using KOMPSAT-2 Imagery  

Kang, Nam Yi (Department of Civil Engineering, Chonbuk National University)
Go, Sin Young (Department of Civil Engineering, Chonbuk National University)
Cho, Gi Sung (Department of Civil Engineering, Chonbuk National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.21, no.2, 2013 , pp. 19-25 More about this Journal
Abstract
Recently, the high-resolution satellite images is used the land cover and status data for the natural resources or environment management very helpful. The SVM algorithm of image processing has been used in various field. However, classification accuracy by SVM algorithm can be changed by various kernel functions and parameters. In this paper, the typical kernel function of the SVM algorithm was applied to the KOMPSAT-2 image and than the result of land cover performed the accuracy analysis using the checkpoint. Also, we carried out the analysis for selected the SVM kernel function from the land cover of the target region. As a result, the polynomial kernel function is demonstrated about the highest overall accuracy of classification. And that we know that the polynomial kernel and RBF kernel function is the best kernel function about each classification category accuracy.
Keywords
High Resolution Satellite Image; KOMPSAT-2; Land-cover Classification; SVM; Kernel Function;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Carl Staelin, 2003, Parameter selection for SVMs, Hewlett-Packard Company.
2 Chang, Q., Chen, Q., Wang, X., 2005, Scaling gaussian RBF kernel width to improve SVM classification, ICNN&B 05' International Conference, Vol. 1.
3 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.   DOI   ScienceOn
4 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.   과학기술학회마을
5 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.   DOI   ScienceOn
6 Han, Seung Hee, 2010, Spatial Information engineering, Goomi Book.
7 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.
8 Kim, Gi Sung, 2003, Classification using support vector machine, Thesis, Inha University.
9 Kim, Hyo Mi, 2002, Classification of multi-class micro array gene expression data using SVM, Thesis, Yonsei University.
10 Lee, Chang Seok, 2011, Adult image detection based on the skin region distribution using SVM, Thesis, Hanbat National University.
11 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.
12 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.
13 Schowengerdt, R., 1983, Techniques of image processing and classification in remote sensing, 1st Ed, pp. 1-58, Academic Press.
14 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.   DOI
15 Richards, John A., 1994, Remote sensing digital image analysis : An introduction, second, Revised and Enlarged Edition, pp.229-262, Springer-Verlag.
16 Scholkopf, Bernhard., Smola, Alexander J., 2002, Leaning with kernels, The MIT Press, London.
17 Vapnik, Vladimir N., 1995, The nature of statical learning theory, Springer-Verlag, NewYork.