DEM generation from contours and a low-resolution DEM

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 134; pp. 135 - 147
• Main Authors: Li, Xinghua, Shen, Huanfeng, Feng, Ruitao, Li, Jie, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2017
• Subjects: Contours; Dictionary learning; Digital elevation model (DEM); Sparse representation; Spatial regularization; Digital elevation model (DEM); Sparse representation; Spatial regularization; Dictionary learning; Contours
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2017.09.014

A digital elevation model (DEM) is a virtual representation of topography, where the terrain is established by the three-dimensional co-ordinates. In the framework of sparse representation, this paper investigates DEM generation from contours. Since contours are usually sparsely distributed and closely related in space, sparse spatial regularization (SSR) is enforced on them. In order to make up for the lack of spatial information, another lower spatial resolution DEM from the same geographical area is introduced. In this way, the sparse representation implements the spatial constraints in the contours and extracts the complementary information from the auxiliary DEM. Furthermore, the proposed method integrates the advantage of the unbiased estimation of kriging. For brevity, the proposed method is called the kriging and sparse spatial regularization (KSSR) method. The performance of the proposed KSSR method is demonstrated by experiments in Shuttle Radar Topography Mission (SRTM) 30 m DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m global digital elevation model (GDEM) generation from the corresponding contours and a 90 m DEM. The experiments confirm that the proposed KSSR method outperforms the traditional kriging and SSR methods, and it can be successfully used for DEM generation from contours.