An integrated method for DEM simplification with terrain structural features and smooth morphology preserved

ABSTRACT

As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations.

KEYWORDS:

• Terrain feature
• DEM
• filter
• cartography

Acknowledgments

The authors are grateful to the associate editor, Shawn Laffan, and the anonymous referees for their valuable comments and suggestions.

Supplementary Material

Supplemental data for this article can be accessed here.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in [figshare.com] with the identifiers (https://doi.10.6084/m9.figshare.11948742.v6) and (https://doi.10.6084/m9.figshare.10318643.v6).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41701440, 41531180]; Natural Science Foundation of Hubei Province [2018CFB513]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170640]; National Key Research and Development Program of China [2017YFB0503500]; a grant from State Key Laboratory of Resources and Environmental Information System [201801].

Notes on contributors

Wenhao Yu

Wenhao Yu is an Associate Professor in the School of Geography and Information Engineering, China University of Geosciences, Wuhan, China. His research interests include spatial database, map generalization, and spatial data mining. He is an editorial board member of PLOS ONE.

Yifan Zhang

Yifan Zhang is a master student in the School of Geography and Information Engineering, China University of Geosciences, Wuhan, China. His research interests include map generalization and spatial analysis.

Tinghua Ai

Tinghua Ai is a Professor in the School of Resource and Environment Science, Wuhan University, Wuhan, China. His research interests include spatio-temporal data mining and map generalization.

Zhanlong Chen

Zhanlong Chen is an Associate Professor in the School of Geography and Information Engineering, China University of Geosciences, Wuhan, China. His research interests include spatio-temporal Big Data and high-performance spatial computing.