A multiple-point geostatistical method for digital elevation models conflation

Abstract

A data conflation method was developed based on a multiple-point geostatistical method. Geostatistics can quantify cross-correlation from different sources of data when integrating geospatial information. Multiple-point geostatistics (MPG) is a development of geostatistics. Pattern-based MPG can capture similar patterns using spatial correlation in the form of training image, and then reproduces the area of interest in a local window using sequential simulation. In the proposed method, MPG simulation was applied to the traditional geostatistical prediction. This method was tested on digital elevation models (DEMs). The aim was to simulate an image at a fine spatial resolution by conflating sparsely sampled elevation data and digital raster elevation at a coarser spatial resolution. The MPG simulation approach was compared to traditional geostatistical conflation using four different methods. The results show that the proposed method can achieve a more precise prediction than the benchmarks.