A terrain-based hybrid approach towards DEM interpolation

Abstract

Visualization of geospatial entities generally adopts digital elevation models (DEMs) that are interpolated to establish 3D co-ordinates for the entire terrain. The accuracy of the generated terrain models depends on the type of interpolation mechanism adopted. Generally available methods in this context use point data values and their distribution; however, terrain characteristics such as shape are not generally considered in the interpolation. We propose an adaptive interpolation strategy that considers the terrain features for improving the estimation accuracy. In this research, we have compared the proposed approach with different contemporary interpolation techniques, such as inverse distance weighting, kriging (KRG), nearest neighbour and spline methods. Comparative analysis of the proposed technique with contemporary approaches revealed that considerable success has been achieved with the procedure. Investigations have also been done to analyse the sensitivity of the approach to terrain smoothness.

Influencing Factors of Submarine Scouring and Siltation Changes in Offshore Area of Shandong Peninsula

“…In addition, it establishes the two-phase water-depth digital elevation models (DEM) according to the determined research areas (Figures 2 and 3). In general, many spatial interpolation methods are used [22][23][24], and Table 2 compares these approaches. Inverse distance weighted (IDW), kriging, and triangular irregular network (TIN) are three common algorithms, while others are typically not used for water-depth elevation interpolation.…”

“…Inverse distance weighted (IDW) is unsuitable for interpolating water depth because the dots are not evenly distributed [25]. Kriging interpolation has a good interpolation effect [22,26]. A triangular irregular network can cause the terrain to appear triangular and artificial due to the TIN's construction [26][27][28].…”