Fusion of Urban TanDEM-X raw DEMs using variational models

TL;DR

This paper explores advanced variational models like TV-L1 and Huber for fusing TanDEM-X raw DEM tiles, demonstrating significant improvements over traditional weighted averaging, especially in urban areas with complex edges.

Contribution

It introduces the application of variational models for DEM fusion, showing their superiority over weighted averaging in urban terrain and across different baseline configurations.

Findings

Variational models outperform weighted averaging in DEM fusion.

DEM quality improves by up to 2 meters in urban areas.

Variational approaches are effective across different data configurations.

Abstract

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes < 20%) and better than 4m 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 Interferometric 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-L1 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 2m particularly in inner-city subsets.