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Research on multi⁃source DEM generation and fusion method based on unmanned aerial vehicle and satellite remote sensing
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Siyu WANG1, Yuguo CUI2, Chunfeng WEI2, Jichi CHEN2
Metrology & Measurement Technology | 2025, 45(6) : 105 - 115
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Metrology & Measurement Technology | 2025, 45(6): 105-115
Theory and Method
Research on multi⁃source DEM generation and fusion method based on unmanned aerial vehicle and satellite remote sensing
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Siyu WANG1, Yuguo CUI2, Chunfeng WEI2, Jichi CHEN2
Affiliations
  • 1Shenyang Geotechnical Investigation Surveying Research Institute Co., Ltd., Shenyang110004, China
  • 2School of Mechanical Engineering, Shenyang University of Technology, Shenyang110870, China
Published: 2025-12-28 doi: 10.11823/j.issn.1674-5795.2025.06.09
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To improve the quality and efficiency of Digital Elevation Model (DEM) construction in complex terrain, this study proposes a multi⁃source DEM acquisition and fusion method that integrates high⁃resolution optical imagery and interferometric Synthetic Aperture Radar (SAR) imagery. Using an unmanned aerial vehicle and satellite remote sensing system as a platform, this method constructs a multi⁃view data acquisition chain to generate optical imagery DEM and interferometric imagery SAR⁃DEM, respectively. By introducing a point cloud classification algorithm based on texture and structural features and a regional adaptive weight estimation model, weighted fusion of multi⁃source elevation data has been achieved. The fusion process employs error constraints and seamline control strategies to address typical challenges such as terrain occlusion, data holes, and elevation jumps. Experiments in representative landforms, including forests, glaciers, deserts, cities, and water bodies, demonstrates that this method has the characteristics of high elevation restoration accuracy and good boundary continuity, and can meet the three dimensions modeling needs of various landform types. Among them, the relative elevation mean error in hilly areas is 0.5 m. The research findings provide stable and reliable technical support for fields such as high⁃resolution topographic mapping, landform evolution monitoring, and disaster early warning, and are of great significance for promoting the automation and intelligence of remote sensing mapping.

DEM  /  unmanned aerial vehicle remote sensing  /  satellite remote sensing  /  SAR  /  optical imagery  /  point cloud classification algorithm  /  multi⁃source data fusion  /  remote sensing mapping
Siyu WANG, Yuguo CUI, Chunfeng WEI, Jichi CHEN. Research on multi⁃source DEM generation and fusion method based on unmanned aerial vehicle and satellite remote sensing[J]. Metrology & Measurement Technology, 2025 , 45 (6) : 105 -115 . DOI: 10.11823/j.issn.1674-5795.2025.06.09
Year 2025 volume 45 Issue 6
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Article Info
doi: 10.11823/j.issn.1674-5795.2025.06.09
  • Receive Date:2025-08-07
  • Online Date:2026-04-09
  • Published:2025-12-28
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  • Received:2025-08-07
  • Revised:2025-09-05
Affiliations
    1Shenyang Geotechnical Investigation Surveying Research Institute Co., Ltd., Shenyang110004, China
    2School of Mechanical Engineering, Shenyang University of Technology, Shenyang110870, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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