Digital terrain model height estimation using support vector machine regression

Abstract

Digital terrain model interpolation is intrinsically a surface fitting problem, in which unknown heights H are estimated from known X-Y coordinates. Notable methods of digital terrain model interpolation include inverse distance to power, local polynomial, minimum curvature, modified Shepard's method, nearest neighbour and polynomial regression. We investigated the support vector machine regression (SVMR) as a new alternative method to these models. SVMR is a contemporary machine learning algorithm that has been applied to several real-world problems aside from digital terrain modelling. The SVMR results were compared with those from notable parametric (the nearest neighbour) and non-parametric (the artificial neural network) techniques. Four categories of error analysis were used to assess the accuracy of the modelling: minimum error, maximum error, means error and standard error. The results indicate that SVMR furnished the lowest error, followed by the artificial neural network model. The SVMR also produced the smoothest surface followed by the artificial neural network model. The high accuracy furnished by SVMR in this experiment attests that SVMR is a promising model for digital terrain model interpolation.