Semivariogram fitting based on SVM and GPR for DEM interpolation

Abstract

DEM (Digital Elevation Model) as a digital model of the earth’s surface elevation could be generated from remote sensing technology such as stereo imaging for various applications. To generate DEM from stereo imagery, interpolation or approximation process stage is required. Stochastic interpolation e.g. ordinary kriging uses semivariogram fitting to calculate weights of interpolation values based on known points. This research is applying regression types of machine learning for semivariogram fitting to interpolate DEM. Previous research conducted was LS-SVM (Least Square-Support Vector Machine) and SVR (Support Vector Regression) for semivariogram fitting process. Types of SVM and GPR (Gaussian Process Regression) are adopted for semivariogram fitting for ordinary kriging interpolation in this experiment. The result showed that in general SVM types could predict accuracy better than other types of regression, and GPR types produce better DEM accuracy based on the experiment.