Structure tensor-based interpolation for the derivation of accurate digital elevation models

Abstract

For many geoscience applications, the quality of digital elevation models (DEMs) is the first and foremost requirement. DEM quality generally depends on the interpolation procedure, especially in forested areas with complex terrain. However, classical interpolation methods do not always preserve terrain features, owing to their isotropic and local estimation nature. Therefore, this paper presents a feature-preserving interpolation method, where a structure tensor is integrated into the radial basis function to ensure its anisotropy. This method alleviates the influence of points located on different surface patches. The performance of the proposed method was compared with those of classical interpolation methods, including the inverse distance weighting (IDW), ordinary kriging (OK), topo to raster (ANUDEM), and natural neighbour (NN) methods. The performance of these methods was tested on benchmark data provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Commission, and on one private dataset. Both datasets were collected via the airborne light detection and ranging (LiDAR) technique. The results from both datasets demonstrate that from a quantitative perspective, the proposed method produces more accurate DEMs than the classical interpolation methods. Specifically, in terms of root mean square error (RMSE), the proposed method is at least 15.8% and 7.6% more accurate when applied to the ISPRS and private data, respectively. Moreover, the proposed method produces more visually appealing surfaces with a good trade-off between terrain feature retention and noise removal.