TY - JOUR
T1 - Semivariogram fitting based on SVM and GPR for DEM interpolation
AU - Setiyoko, A.
AU - Arymurthy, Aniati Murni
AU - Basaruddin, T.
AU - Arief, R.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071942132&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/311/1/012076
DO - 10.1088/1755-1315/311/1/012076
M3 - Conference article
AN - SCOPUS:85071942132
SN - 1755-1307
VL - 311
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012076
T2 - Padjadjaran Earth Dialogues: International Symposium on Geophysical Issues, PEDISGI 2018
Y2 - 2 July 2018 through 4 July 2018
ER -