A high-precision modeling and error analysis method for mountainous and canyon areas based on TLS and UAV photogrammetry

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 1 - 16
• Main Authors: Luo, Xiang-long, Jiang, Nan, Li, Hai-bo, Xiao, Huai-xian, Chen, Xing-zhen, Zhou, Jia-wen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Aerial photography; Algorithms; Analytical models; Atmospheric modeling; Autonomous aerial vehicles; Canyons; Data models; Error analysis; Hydroelectric power; Hydroelectric power generation; Hydroelectric power stations; Laser radar; Mapping; Model accuracy; Modelling; Mountain regions; Mountainous areas; Mountains; Photogrammetry; Point cloud compression; Terrain mapping; terrain modeling; Terrain models; terrestrial laser scanning (TLS); Three dimensional models; TLS; UAV; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Yebatan
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3382092

Obtaining comprehensive and accurate terrain data is important for engineering construction. Unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are two widely used terrain modeling techniques. In mountainous areas, both techniques suffer limitations. These limitations occur in uninhabited areas, primarily caused by the steep terrain and inconvenient transportation conditions, resulting in poor data integrity and inadequate accuracy in UAV and TLS terrain mapping. In this article, we proposed a fusion modeling method based on UAV photogrammetry and TLS for high-precision terrain mapping in mountainous and canyon areas. The proposed method entails the use of TLS data to provide additional control points for UAV modeling, resulting in an improved accuracy of the modeling results. In addition, to quantify the optimization effect of this method, we proposed a 3D model deviation comparison method based on the iterative closest point (ICP) algorithm. This method can be employed to accurately depict the differences in distance and rotation angle between multiple terrain models. We applied this method to the Yebatan hydropower station in Southwest China, which increased the accuracy of the terrain data by 26% and expanded the effective range by over 100%.