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3 October 2019 An assessment of support vector machine for land cover classification over South Korea
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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%.
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S. Son, S. Park, S. Lee, S. Kim, J. Han, and J. Kim "An assessment of support vector machine for land cover classification over South Korea", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111560E (3 October 2019); https://doi.org/10.1117/12.2533045
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