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Rapid identification of landslide,collapse and crack based on low-altitude remote sensing image of UAV 被引量:12

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摘要 Landslides,collapses and cracks are the main types of geological hazards,which threaten the safety of human life and property at all times.In emergency surveying and mapping,it is timeconsuming and laborious to use the method of field artificial investigation and recognition and using satellite image to identify ground hazards,there are some problems,such as time lag,low resolution,and difficult to select the map on demand.In this paper,a10 cm per pixel resolution photogrammetry of a geological hazard-prone area of Taohuagou,Shanxi Province,China is carried out by DJ 4 UAV.The digital orthophoto model(DOM),digital surface model(DSM) and three-dimensional point cloud model(3 DPCM) are generated in this region.The method of visual interpretation of cracks based on DOM(as main)-3 DPCM(as auxiliary) and landslide and collapse based on 3 DPCM(as main)-DOM and DSM(as auxiliary) are proposed.Based on the low altitude remote sensing image of UAV,the shape characteristics,geological characteristics and distribution of the identified hazards are analyzed.The results show that using UAV low altitude remote sensing image,the method of combination of main and auxiliary data can quickly and accurately identify landslide,collapse and crack,the accuracy of crack identification is 93%,and the accuracy of landslide and collapse identification is 100%.It mainly occurs in silty clay and mudstone geology and is greatly affected by slope foot excavation.This study can play a great role in the recognition of sudden hazards by low altitude remote sensing images of UAV.
出处 《Journal of Mountain Science》 SCIE CSCD 2020年第12期2915-2928,共14页 山地科学学报(英文)
基金 supported by the National Natural Science Foundation of China (Award Number: 51704205) Key R & D Plan projects in Shanxi Province of China (Award Number: 201803D31044) Education Department Natural Science Foundation in Guizhou of China (Award Number: KY (2017) 097) the High-Level Talents Fund of Guizhou University of Engineering Science (Award Number: G2015005)。
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