A basin recognition method by landform classification and geometrical feature discrimination
Source abstract
Abstract
Aiming at the geomorphological and geometrical features of the basin, a recognition approach combined with a landform classification and discriminant analysis is presented. First, a k-means algorithm is applied to the digital elevation model data. The basic types of landforms with elevation, slope, and relief amplitude are clustered, and the landform regions of plains, mountains, and hills have been separated. Focusing on the plain and its neighboring region, a measurement function is defined to analyze the cases of the plain surrounded by mountains and hills. Finally, a discrimination model for basin recognition is presented with parameters of the surrounded state, area of the plain, and difference of its elevation. The approach is applied to the DEM of the Shuttle Radar Topography Mission of China. The experiments show that the results are the same as those of real terrains.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrocarbon exploration and reservoir analysis · Geochemistry and Geologic Mapping · Automated Road and Building Extraction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information and Control Engineering, Xi’an University of Architecture and Technology 1 , No.13 Tanta Road, Xi’an 710055, China
State Key Laboratory of Geo-Information Engineering 2 , No.1 Tanta Road, Xi’an 710055, China
Funding
Foundation of State Key Laboratory of Geo-Information Engineering · National Science Foundation of Shaan Xi Province · National Key R&D Program of China
References (21)
Show source-provided references
1. Landforms and landform elements in geomorphometry10.1016/s0166-2481(08)00009-3 ↗
2. Multi-modal deep learning for landform recognition10.1016/j.isprsjprs.2019.09.018 ↗
3. Progress of DEM and digital terrain analysis in China10.11821/dlxb201409006 ↗
4. Supervised landform classification of Northeast Honshu from DEM-derived thematic maps10.1016/j.geomorph.2006.02.005 ↗
5. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping10.1016/j.rse.2011.05.007 ↗
6. 1984 · 33
7. DEM based auto-classification of Chinese landform10.3969/j.issn.1560-8999.2006.04.003 ↗
8. Basic terrestrial geomorphological types in China and their circumscriptions10.3321/j.issn:1001-7410.2008.04.004 ↗
9. DEM based auto-extraction of geomorphic units10.3724/SP.J.1047.2008.00088 ↗
10. Research on the classification system of digital land geomorphology of 1:1 000 000 in China10.3724/SP.J.1047.2009.00707 ↗
11. Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines10.1016/j.geomorph.2017.05.013 ↗
12. Analysis of stratification patterns in river-influenced mesotidal and macrotidal estuaries using 3D hydrodynamic modeling and K-means clustering10.1016/j.ecss.2016.08.005 ↗
13. Variable weighting in fuzzy k-means clustering to determine the number of clusters10.1109/tkde.2019.2911582 ↗
14. Research on geomorphological regionalization of China10.11821/xb201303001 ↗
15. 1987
16. Harbor target recognition based on inside region
17. 2010 · 9
18. The compilation design of atlas of physical Geography of China10.1016/S1874-8651(10)60080-4 ↗
19. 1956 · 21
20. A research on the mathematical definition of the basic landform shape10.3771/j.issn.1009-2307.2002.03.005 ↗
21. A. Jarvis, H. I. Reuter, A. Nelson, and E. Guevara, Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), http://srtm.csi.cgiar.org/srtmdata/, 2008.