Fusion of Digital Elevation Models from Various Data Sources

Abstract

In recent years, collection and processing techniques for Digital Elevation Models (DEMs) generation have improved rapidly, allowing surfaces to be represented with more detail and accuracy. Fusion of overlapping DEMs, generated from data captured with different acquisition techniques, or at different times, allows to find inconsistencies, improve density, accuracy and currency, and eliminate gaps. This is of crucial importance for the improvement of global DEMs, like SRTM (Shuttle Radar Topography Mission). Since the DEMs may have substantial differences, simple amalgamation of all available points would not be satisfying and it would degrade the accuracy of the merged model. Any integration approach aiming at high-quality models needs an increased level of robustness. Computational efficiency and global convergence are further preferable properties. In this paper, an approach is presented for DEM fusion. The goal is to use existing DEMs to create automatically a new DEM which is: geometrically accurate by depicting the correct height information of the area, clean by eliminating blunders and errors which are present in the initial data and complete by modelling all the area on the highest possible resolution. The method is presented and the first results achieved after fusing a Lidar-based DEM and an IKONOS-based DEM on Thun, Switzerland, are shown and commented.