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Proceedings Paper

An assessment of support vector machine for land cover classification over South Korea
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Paper Abstract

Information on land cover is very important variable not only affecting on human activities but also studying the functional and morpho-functional changes occurring in the earth. The goal of this study is an assessment of support vector machine (SVM) for land cover classification over South Korea using normalized difference vegetation index (NDVI) of geostationary ocean color imager (GOCI). We collected level-2 land cover maps in South Korea and defined the seven most common land cover types (urban, croplands, forest, grasslands, wetlands, barren, and water) in South Korea to assess SVM model and produce land cover map. To train SVM model, we decided 1,000 training samples per classes. In addition, We repeated 50 times random selection of training samples. In order to evaluate accuracy of SVM`s kernels, we selected four kernels; linear, polynomial, sigmoid, and radial basis function (RBF). The parameters of each kernel were determined by the grid-search method using cross validation approach. The best accuracy of four kernel is linear kernel, the overall accuarcy was calculated 71.592%.

Paper Details

Date Published: 3 October 2019
PDF: 6 pages
Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111560E (3 October 2019);
Show Author Affiliations
S. Son, Pukyong National Univ. (Korea, Republic of)
S. Park, Pukyong National Univ. (Korea, Republic of)
S. Lee, Pukyong National Univ. (Korea, Republic of)
S. Kim, Pukyong National Univ. (Korea, Republic of)
J. Han, Pukyong National Univ. (Korea, Republic of)
J. Kim, Pukyong National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11156:
Earth Resources and Environmental Remote Sensing/GIS Applications X
Karsten Schulz; Ulrich Michel; Konstantinos G. Nikolakopoulos, Editor(s)

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