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

摘要

Information on land cover is very important variable not only affecting on human activities but also studying the functionaland 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) ofgeostationary ocean color imager (GOCI). We collected level-2 land cover maps in South Korea and defined the sevenmost common land cover types (urban, croplands, forest, grasslands, wetlands, barren, and water) in South Korea to assessSVM 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 selectedfour kernels; linear, polynomial, sigmoid, and radial basis function (RBF). The parameters of each kernel were determinedby the grid-search method using cross validation approach. The best accuracy of four kernel is linear kernel, the overallaccuarcy was calculated 71.592%.