Fusion of Urban TanDEM-X Raw DEMs Using Variational Models

Abstract

Recently, a new global digital elevation model (DEM) with pixel spacing of 0.4 arcsec and relative height accuracy finer than 2 m for flat areas (slopes $ 20%) and better than 4 m for rugged terrain (slopes $ >$ 20%) was created through the TanDEM-X mission. One important step of the chain of global DEM generation is to mosaic and fuse multiple raw DEM tiles to reach the target height accuracy. Currently, weighted averaging (WA) is applied as a fast and simple method for TanDEM-X raw DEM fusion, in which the weights are computed from height error maps delivered from the Integrated TanDEM-X Processor (ITP). However, evaluations show that WA is not the perfect DEM fusion method for urban areas, especially in confrontation with edges such as building outlines. The main focus of this paper is to investigate more advanced variational approaches such as TV- $ L_{1}$ and Huber models. Furthermore, we also assess the performance of variational models for fusing raw DEMs produced from data takes with different baseline configurations and height of ambiguities. The results illustrate the high efficiency of variational models for TanDEM-X raw DEM fusion in comparison to WA. Using variational models could improve the DEM quality by up to 2 m, particularly in inner city subsets.