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Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry

Journal of mountain Science 2024年第4期21卷 1190-1214页

作者： BI Rui GAN Shu YUAN Xiping LI Kun LI Raobo LUO Weidong CHEN Cheng GAO Sha HU Lin ZHU Zhifu School of Land and Resources Engineering Kunming University of Science and TechnologyKunming 650093China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming 650093China West Yunnan University of Applied Sciences Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities in Yunnan ProvinceDali 671006China

High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide *** study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route *** effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics *** results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement *** 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface *** slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and *** elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation *** the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface *** route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous *** geomorphological analysis methods used will provide references for identifying and describing topographic

关键词： UAV Landslide Vertical route SfM-MVS Topographic features

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An Effective and Robust Method for Describing 3d Local Surfaces Based on the Locally Weighted Angular Images

SSRN

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SSRN 2022年

作者： Li, Raobo Gan, Shu Yuan, Xiping Bi, Rui Gao, Sha Luo, Weidong Hu, Lin School of Land and Resources Engineering Kunming University of Science and Technology Kunming650093 China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming650093 China Key Laboratory of Mountain Real Scene Point Cloud Data Processing Application for Universities West Yunnan University of Applied Sciences Dali671006 China

This paper proposes a repeatable local reference system (LRF) and a locally weighted angle image (LWAI) based on the LRF to achieve a comprehensive description of the feature ***, z-axis is estimated based on the weighted covariance matrix constructed from some points in the neighborhood of a key point, and x-axis is done using lpspatial mechanics model, the y-axis is represented by the cross product of the x-axis and z-axis. Then, the weighted angles of the transformed constructed line to the 2D plane are estimated by rotating the neighboring points of a key point to the LRF, and a set of statistics of these weighted angles is defined as LWAI. Finally, we compared LWAI with several state-of-the-art 3D feature descriptors, the descriptiveness, robustness and effectiveness of the method are verified. In addition, LWAI was also applied on point cloud registration, and the registration results again validated the effectiveness of LWAI. © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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A Compact and Robust 3d Local Feature Descriptor Based on Cumulative Multi-Feature Images

SSRN

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SSRN 2023年

作者： Li, Raobo Yuan, Xiping Gan, Shu Bi, Rui Gao, Sha Luo, Weidong Hu, Lin Chen, Cheng School of Land and Resources Engineering Kunming University of Science and Technology Kunming650093 China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming650093 China Key Laboratory of Mountain Real Scene Point Cloud Data Processing Application for Universities West Yunnan University of Applied Sciences Dali671006 China

The 3D local surface description is a crucial aspect within the domain of computer vision. This paper advances a novel approach that leverages a repeatable local reference frame (LRF) and a cumulative multi-feature images (CMFI) for a more thorough representation of the feature space. The method starts by estimating the z-axis of the LRF using a weighted covariance matrix derived from the partial points in the neighborhood of the key points, while the x-axis is calculated through the utilization of spatial mechanics model. Subsequently, the neighboring points of the key points are projected onto three orthogonal planes, the spatial features (point cloud density, depth and horizontal distance) of the projected points are estimated, and the statistics of these features are defined as CMFI after accumulation. Finally, the performance of the CMFI is analyzed comprehensively through comparison with state-of-the-art descriptors, with the results demonstrating the descriptiveness and compactness of the CMFI. © 2023, The Authors. All rights reserved.

关键词： Covariance matrix

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