Fusion of multi-scale DEMs using a regularized super-resolution method

Abstract

The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.

Keywords:

• multi-scale DEMs
• data fusion
• regularized framework
• super-resolution

Acknowledgements

The authors thank the editors and the anonymous reviewers for their valuable suggestions. They also thank Dr Xianwei Zheng and Dr Lei Shi for their help with data preparation in this work.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China [grant number 41422108], [grant number 41401383]; Wuhan Science and Technology Program [grant number 2013072304010825]; Program for Changjiang Scholars and Innovative Research Team in University [grant number IRT1278].