Skip to main content
Log in

Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Terrain feature extraction is one of the critical issues in geographic information science. As important terrain feature lines, ridge lines and valley lines play an important role in hydrological analysis, terrain reconstruction and automatic integration of contour lines. But the extraction of terrain feature lines is complicated and time-consuming task. In this paper, a terrain feature line extraction method is proposed based on clustering technique. The terrain feature points are automatically extracted according to the agglomeration of terrain points, and the similar points are automatically identified according to the DBSCAN clustering algorithm. The points with high similarity are clustered along the direction of ridge or valley, and the whole terrain will be clustered into multiple sub-regions. The nearest sub-regions are found by calculating the minimum distance between these sub-regions, the adjacent sub-regions are connected orderly by their center line to obtain terrain feature lines. Compared with other methods, the cluster analysis method in this paper has simple process and high efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The terrain data and codes that support the findings of this study are available in figshare with the identififier doi:10.6084/m9.figshare.21078646.

References

  • Ai TH, Li JZ (2010) A DEM generalization by minor valley branch detection and grid filling. ISPRS J Photogramm Remote Sens 65(2):198–207

    Article  Google Scholar 

  • Blashfield RK, Aldenderfer MS (1978) The literature on cluster analysis. Multivar Behav Res 13(3):271–295

    Article  Google Scholar 

  • Busu M, Nedelcu C, Cadis A (2021) An overview of the academic level among EU countries. A cluster analysis approach. Proceedings Int Conf Bus Excell 15(1):210–217

    Article  Google Scholar 

  • Chang Y-C, Sinha G (2007) A visual basic program for ridge axis picking on DEM data using the profile-recogintion and polygon-breaking algorithm. Comput Geosci 33(2):229–237

    Article  Google Scholar 

  • Chang YC, Song GS, Hsu SK (1998) Automatic extraction of ridge and valley axes using the profile recognition and polygon-breaking algorithm. Comput Geosci 24(1):83–93

    Article  Google Scholar 

  • Daya Sagar BS, Murthy MBR, Babu Rao C, Raj B (2003) Morphological approach to extract ridge and valley connectivity networks from Digital Elevation Models. Int J Remote Sens 24(3):573–581

    Article  Google Scholar 

  • Dong YL, Guo W, Zha FS, Liu YZ, Chen C, Sun LL (2020) A vision-based two-stage framework for inferring physical properties of the terrain. Appl Sci 10:6473

    Article  Google Scholar 

  • Fisher P, Wood J, Cheng T (2004) Where Is Helvellyn? Fuzziness of Multi-Scale Landscape Morphometry. Trans Inst Br Geogr 29(1):106–128

    Article  Google Scholar 

  • Hinnell AC, Ferré TPA, Vrugt JA, Huisman JA, Moysey S, Rings J, Kowalsky MB (2010) Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion. Water Resour Res 46(4):1–14

    Article  Google Scholar 

  • Hu QM (2017) Research progress of topographic feature line extraction. Surveying and Mapping and Spatial Geographic Information 40(4):47–50

    Google Scholar 

  • Khan K, Rehman SU, Aziz K, Fong S, Sarasvady S (2014) DBSCAN: Past, present and future. The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Bangalore, p 232–238. https://doi.org/10.1109/ICADIWT.2014.6814687

  • Li M, Bi X, Wang L, Han XM (2021) A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm. Comput Commun 167:75–84

    Article  Google Scholar 

  • Masavi M, Natarajan P, Binello S, McNeely J (1999) Knowledge based extraction of ridge lines from digital terrain elevation data. IEEE International Geoscience and Remote Sensing Symposium, IGARSS’99 (Cat. No. 99CH36293). IEEE 5:2492–2494

    Google Scholar 

  • Maurya R K, Kulkarni S T, Kalita N(2018) Projection-based spatial morphology for extracting ridge and valley profiles of mountains from 3D amorphous data. IEEE International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, Dec. 14–15, pages 1–6

  • Milligan GW, Cooper MC (1987) Methodology review: Clustering methods. Appl Psychol Meas 11(4):329–354

    Article  Google Scholar 

  • Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg RD, Mukhopadhyay A (2013) Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). Int J Appl Earth Obs Geoinf 21:205–217

    Google Scholar 

  • O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing 28(3):323–344

    Article  Google Scholar 

  • Pang XF, Song Z, Xie WY (2013) Extract valley-ridge lines from point-cloud-based 3D fingerprint models. IEEE Computer Graphics & Application 33(4):73–81

    Article  Google Scholar 

  • Peucker TK, Douglas DH (1975) Detection of surface-specific points by local parallel processing of discrete terrain elevation data. Comput Graphics Image Process 4(4):375–387

    Article  Google Scholar 

  • Riazanoff S, Cervelle B, Chorowicz J (1988) Ridge and valley line extraction from digital terrain models. Int J Remote Sens 9(6):1175–1183

    Article  Google Scholar 

  • Romero BE, Clarke KC (2018) Exploring uncertainties in terrain feature extraction across multi-scale, multi-feature, and multi-method approaches for variable terrain. Cartogr Geogr Inf Sci 45(5):381–399

    Article  Google Scholar 

  • Schubert E, Sander J, Ester M, Kriegel HP (2017) DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS) 42(3):1–21

    Article  Google Scholar 

  • Stanislawski LV, Shavers EJ, Wang S, Jiang Z, Usery EL, Moak E, Duffy A, Schott J (2021) Extensibility of U-Net neural network model for hydrographic feature extraction and implications for hydrologic modeling. Remote Sensing 13(12):2368

    Article  Google Scholar 

  • Tang GA, Yang WY, Yang X, Liu AL, Yi HW, Li XY (2003) Discussion on several problems in quantitative factor mining of DEM terrain. Science of Surveying and Mapping 28(1):28–32

    Google Scholar 

  • Tesfa TK, Tarboton DG, Watson DW, Schreuders KAT (2011) Extraction of hydrological proximity measures from DEMs using parallel processing. Environ Model Softw 26(12):1696–1709

    Article  Google Scholar 

  • Wood J (1996) The geomorphological characterisation of digital elevation models.  Ph.D. Thesis, University of Leicester, Leicester, p 466

  • Wu J, Dong YF, Peng J, Dai XP (2018) Integrated extraction of mountain apex and saddle point based on DEM. Geomatics and Information Technology 43(5):85–88

    Google Scholar 

  • Zhang L, Tang GA, LI F Y, Xiong L Y. (2012) A review of research along the loess ditch. Geography and Geo-Information Science 28(06):44–48

    Google Scholar 

  • Zhou W, Peng RC, Dong J, Wang T (2018) Automated extraction of 3D vector topographic feature line from terrain point cloud. Geocarto Int 33(10):1036–1047

    Article  Google Scholar 

  • Zhou Q, Su J, Arnbjerg-Nielsen K, Ren Y, Luo JH, Ye ZJ, Feng JM (2021) A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment. Water 13(11):1483

    Article  Google Scholar 

  • Zhou W, Peng R C, Zhang L H, Wang W J (2016) Topographic feature line extraction from point cloud based on SSV and HC-laplacian smoothing. Proceedings of International Conference on Advances in Energy and Environment Research (ICAEER), Guangdong, China, Aug. Eds:Bachir Achour and Qiyan Wu, pages, 246–253. CRC Press

Download references

Funding

This work was partly supported by the National Natural Science Foundation of China (No. 41930102, 41771411).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cheng Zhang, Wanfeng Dou and Yuan Pang. The first draft of the manuscript was written by Cheng Zhang. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wanfeng Dou.

Ethics declarations

Competing interests

No competing interests.

Conflict of Interest

No potential conflict of interest was reported by the authors.

Additional information

Communicated by H. Babaie.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Dou, W. & Pang, Y. Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method. Earth Sci Inform 16, 345–356 (2023). https://doi.org/10.1007/s12145-023-00943-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-00943-7

Keywords

Navigation