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Published： 2018-1 ，
Accepted： 22 August 2017
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张纯斌, 杨胜天, 赵长森, 娄和震, 张亦弛, 白娟, 王志伟, 管亚兵, 张远. 2018. 小型消费级无人机地形数据精度验证. 遥感学报, 22(1): 185–195
Zhang C B, Yang S T, Zhao C S, Lou H Z, Zhang Y C, Bai J, Wang Z W, Guan Y B and Zhang Y. 2018. Topographic data accuracy verification of small consumer UAV. Journal of Remote Sensing, 22(1): 185–195
低空遥感是近几年快速发展、应用非常广泛的新兴技术。小型消费级无人机集成可见光传感器
具有快速、灵活、高性价比等优势，受到广泛关注。然而目前有关该类无人机综合测量精度的研究不足，影响其进一步的推广应用。为此，本文开展了针对大疆(Phantom 3 professional)小型消费级无人机地形测量数据精度验证工作，设定6种航高(50 m、60 m、70 m、80 m、90 m和100 m)获取研究区的立体像对，生成影像点云(point cloud)、数字表面模型(DSM)以及数字正射影像图(DOM)等结果。在测量精度验证中，首先，在标准实验场均匀布设地面控制点(GCP)，利用差分GPS测出GCP的高精度3维坐标；然后，通过GCP对立体像对进行绝对定位；最后，利用误差统计方法分析上述结果的测量精度。验证表明，在50—100 m航高时，无人机影像结果的分辨率为2.22—4.23 cm，水平方向平均误差为±0.51 cm，垂直方向平均误差为±4.39 cm，相对均方根误差(RMSE)水平方向为±2.79 cm，垂直方向为±9.98 cm。研究结果表明，小型消费级无人机在飞控系统下的测量精度可达厘米级，这不仅为野外地理和生态调查工作者提供一种低成本、快速、灵活与精确获取地形信息的新型测量手段，同时还对使用此类无人机做航测应用及飞行参数设置提供一定参考。
Low-altitude remote sensing recently became a hot technology with rapid development and com-prehensive application. Small consumer UAV attracted wide attention with rapid
flexible
and cost-effective ad-vantages. Large professional UAV
which is vulnerable to weather conditions
requires professional manipulation and airspace application. These factors restrict its ability to access terrain data agilely and rapidly. A small consumer UAV can compensate for large professional UAV limitations. This study comprehensively verifies the accuracy of the data obtained through this type of UAV to improve application reliability. This study focuses on the precision verification of topographic data obtained by small consumer UAV (Phantom 3 Professional). Six kinds of flight heights (50 m
60 m
70 m
80 m
90 m
and 100 m) are set to acquire a stereoscopic image and generate Point Cloud
Digital Surface Model
and Digital Orthophoto Map. Ground Control Points (GCPs) are laid out uniformly in the standard experimental field to verify measurement accuracy
and their three-dimensional coordinates are derived using differential GPS with high-precision. The absolute position of the stereoscopic images is calibrated by GCPs. Finally
the measurement accuracy of the result is analyzed using the mean error and relative root mean square error (RMSE). Results show that the resolution of the UAV image is 2.22—4.23 cm for flight height 50–100 m and will decrease with increasing flight altitude. The mean error is ± 0.51 cm in the horizontal direction and ± 4.39 cm in the vertical direction. RMSE is ± 2.79 cm in the horizontal direction and ± 9.98 cm in the vertical direction. The errors in horizontal and vertical directions are within normal distribution
but the error range is larger in the vertical direction. Five or more images in the same area are recommended when shooting to avoid errors caused by insufficient image overlap and to generate high-quality data. Simultaneously
GCPs should be evenly laid in the survey area to ensure absolute positioning accuracy and should be found in more than five images. Experimental precision can be influenced by a number of factors
such as light and weather con-ditions and flight stability. The GCP selection
measurement method
and image spike processes include some errors. The research shows that the measurement accuracy of small consumer UAV can reach centimeter level with reliable flight control system condition; this condition provides a new measurement method for low-cost
fast
flexible
and accurate terrain information acquisition to geography and ecology researchers.
小型消费级无人机(UAV)精度验证地形测量差分GPS地面控制点(GCP)
small consumer UAVaccuracy verificationtopographic surveydifferential GPSGCP (Ground Control Points)
Anderson K and Gaston K J. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3): 138–146 10.1890/120150
Bhardwaj A, Sam L, Martín-Torres F J and Kumar R. 2016. UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sensing of Environment, 175: 196–204 10.1016/j.rse.2015.12.029
Car M, Jurić Kaćunić D and Kovačević M S. 2016. Application of unmanned aerial vehicle for landslide mapping//Paar R, Marendić A and Zrinjski M, eds. Proceedings of the International Symposiun on Engineering Geodesy-SIG. Zagreb: Croatian Geodetic Society: 549–559
陈仲新, 任建强, 唐华俊, 史云, 冷佩, 刘佳, 王利民, 吴文斌, 姚艳敏, 哈斯图亚. 2016. 农业遥感研究应用进展与展望. 遥感学报, 20(5): 748–767 10.11834/jrs.20166214
Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748–767 (
Cho S J, Bang E S and Kang I M. 2015. Construction of precise digital terrain model for nonmetal open-pit mine by using unmanned aerial photograph. Economic and Environmental Geology, 48(3): 205–212 10.9719/EEG.2015.48.3.205
Clapuyt F, Vanacker V and Van Oost K. 2016. Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, 260: 4–15 10.1016/j.geomorph.2015.05.011
Dandois J P, Olano M and Ellis E C. 2015. Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, 7(10): 13895–13920 10.3390/rs71013895
Georgopoulos A, Oikonomou C, Adamopoulos E and Stathopoulou E K. 2016. Evaluating unmanned aerial platforms for cultural heritage large scale mapping//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic: ISPRS: 355–362 [DOI: 10.5194/isprs-archives-XLI-B5-355-2016]
Harder P, Schirmer M, Pomeroy J and Helgason W. 2016. Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle. The Cryosphere, 10(6): 2559–2571 10.5194/tc-10-2559-2016
Hodgson J C, Baylis S M, Mott R, Herrod A and Clarke R H. 2016. Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports, 6: 22574 10.1038/srep22574
Jordan B R. 2015. A bird’s-eye view of geology: the use of micro drones/UAVs in geologic fieldwork and education. GSA Today, 25(7): 50–52 10.1130/GSATG232GW.1
Kraaijenbrink P, Meijer S W, Shea J M, Pellicciotti F, De Jong S M and Immerzeel W W. 2016. Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery. Annals of Glaciology, 57(71): 103–113 10.3189/2016AoG71A072
Lee S and Choi Y. 2015. Topographic survey at small-scale open-pit mines using a popular rotary-wing unmanned aerial vehicle (drone). Tunnel and Underground Space, 25(5): 462–469 10.7474/TUS.2015.25.5.462
李德仁. 2016. 论“互联网+”天基信息服务. 遥感学报, 20(5): 708–715 10.11834/jrs.20166222
Li D R. 2016. The “Internet Plus” space-based information services. Journal of Remote Sensing, 20(5): 708–715 (
李德仁, 陈晓玲, 蔡晓斌. 2008.空间信息技术用于汶川地震救灾. 遥感学报, 12(6): 841–851 10.11834/jrs.200806113
Li D R, Chen X L and Cai X B. 2008. Spatial information techniques in rapid response to Wenchuan earthquake. Journal of Remote Sensing, 12(6): 841–851 (
李德仁, 李明. 2014. 无人机遥感系统的研究进展与应用前景. 武汉大学学报(信息科学版), 39(5): 505–513, 540 10.13203/j.whugis20140045
Li D R and Li M. 2014. Research advance and application prospect of unmanned aerial vehicle remote sensing system. Geomatics and Information Science of Wuhan University, 39(5): 505–513, 540 (
刘雪峰, 吕强, 何绍兰, 易时来, 谢让金, 郑永强, 胡德玉, 汪志涛, 邓烈. 2015. 柑橘植株冠层氮素和光合色素含量近地遥感估测. 遥感学报, 19(6): 1007–1018 10.11834/jrs.20155078
Liu X F, Lyu Q, He S L, Yi S L, Xie R J, Zheng Y Q, Hu D Y, Wang Z T and Deng L. 2015. Estimation of nitrogen and pigments content in citrus canopy by low-altitude remote sensing. Journal of Remote Sensing, 19(6): 1007–1018 (
Messinger M, Asner G P and Silman M. 2016. Rapid assessments of amazon forest structure and biomass using small unmanned aerial systems.Remote Sensing, 8(8): 615 10.3390/rs8080615
Neitzel F and Klonowski J. 2011. Mobile 3D mapping with a low-cost UAV system//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Zurich, Switzerland: ISPRS, 38: 1–6
Neugirg F, Stark M, Kaiser A, Vlacilova M, Della Seta M, Vergari F, Schmidt J, Becht M and Haas F. 2016. Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high-resolution terrestrial LiDAR and UAV surveys. Geomorphology, 269: 8–22 10.1016/j.geomorph.2016.06.027
Pierzchała M, Talbot B and Astrup R. 2014. Estimating soil displacement from timber extraction trails in steep terrain: Application of an unmanned aircraft for 3D modelling. Forests, 5(6): 1212–1223 10.3390/f5061212
Ruzgienė B, Berteška T, Gečyte S, Jakubauskienė E and Aksamitauskas V Č. 2015. The surface modelling based on UAV photogrammetry and qualitative estimation[J]. Measurement, 73: 619–627 10.1016/j.measurement.2015.04.018
Turner D, Lucieer A, Malenovský Z, King D H and Robinson S A. 2014a. Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over Antarctic Moss Beds. Remote Sensing, 6(5): 4003–4024 10.3390/rs6054003
Turner D, Lucieer A and Wallace L. 2014b. Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2738–2745 10.1109/TGRS.2013.2265295
Vautherin J, Rutishauser S, Schneider-Zapp K, Choi H F, Chovancova V, Glass A and Strecha C. 2016. Photogrammetric accuracy and modeling of rolling shutter cameras. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(3): 139–146 10.5194/isprs-annals-III-3-139-2016
Vivoni E R, Rango A, Anderson C A, Pierini N A, Schreiner-McGraw A P, Saripalli S and Laliberte A S. 2014. Ecohydrology with unmanned aerial vehicles. Ecosphere, 5(10): 1–14 10.1890/ES14-00217.1
王敏, 张祖胜, 许明元, 周伟, 李鹏, 尤晓青. 2005. 2000国家GPS大地控制网的数据处理和精度评估. 地球物理学报, 48(4): 817–823 10.3321/j.issn:0001-5733.2005.04.013
Wang M, Zhang Z S, Xu M Y, Zhou W, Li P and You X Q. 2005. Data processing and accuracy analysis of national 2000’ GPS geodetic control network. Chinese Journal of Geophysics, 48(4): 817–823 (
Watts A C, Ambrosia V G and Hinkley E A. 2012. Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sensing, 4(6): 1671–1692 10.3390/rs4061671
Xiang HT and Tian L. 2011. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering, 108(2): 174–190 10.1016/j.biosystemseng.2010.11.010
徐冠华, 柳钦火, 陈良富, 刘良云. 2016. 遥感与中国可持续发展: 机遇和挑战. 遥感学报, 20(5): 679–688 10.11834/jrs.20166308
Xu G H, Liu Q H, Chen L F and Liu L Y. 2016. Remote sensing for China’s sustainable development: opportunities and challenges. Journal of Remote Sensing, 20(5): 679–688 (
Zahawi R A, Dandois J P, Holl K D, Nadwodny D, Reid J L and Ellis E C. 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery.Biological Conservation, 186: 287–295 10.1016/j.biocon.2015.03.031
Zarco-Tejada P J, Diaz-Varela R, Angileri V and Loudjani P. 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55: 89–99 10.1016/j.eja.2014.01.004
周洁萍, 龚建华, 王涛, 汪东川, 杨荔阳, 赵向军, 洪宇, 赵忠明. 2008. 汶川地震灾区无人机遥感影像获取与可视化管理系统研究. 遥感学报, 12(6): 877–884 10.3321/j.issn:1007-4619.2008.06.009
Zhou J P, Gong J H, Wang T, Wang D C, Yang L Y, Zhao X J, Hong Y and Zhao Z M. 2008. Study on UAV remote sensing image acquiring and visualization management system for the area affected by 5•12 Wenchuan earthquake. Journal of Remote Sensing, 12(6): 877–884 (
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