利用无人机倾斜影像与GCP构建高精度侵蚀沟地形模型
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摘要
为了提高侵蚀沟立体建模与监测的精度,该文采用消费级无人机作为低空遥感平台,以黄土高原一典型切沟为研究对象,通过无人机采集的倾斜影像与部署的地面控制点,采用多视立体运动恢复结构方法(structure from motion with multi-view stereo,Sf M-MVS)构建了高精度侵蚀沟表面模型,对其建模精度与数字高程模型、正射影像等成果进行分析,并与传统正射航图建模成果进行了比较。结果表明:构建的侵蚀沟稠密点云模型的水平均方根误差约为0.096 m,高程均方根误差约为0.018 m,满足1:500比例尺数字线划图与正射影像图的要求。与正射航图建模成果相比,高程误差减小了50%;侵蚀沟稠密点云的整体密度与地面激光雷达相当,且避免了后者多站拼接造成的密度不均问题。除了沟头部分的小块内凹区域,沟壁、沟头部分没有明显的空洞,植被覆盖的区域也能够正常建模。而正射航图的建模成果中在沟头内凹部分以及植被覆盖部分存在大块的空洞;由侵蚀沟的数字高程模型与等高线图可见,构建的侵蚀沟模型能够准确地反映切沟的形态特征。总体而言,该方法在侵蚀沟的高精度建模与监测方面具有显著优势,具有推广应用的潜力。
In this paper, Sf M-MVS(structure from motion with multi-view stereo) method was introduced to construct a high precision terrain model of the typical gully on the Losses Plateau of northern Shaanxi in China, with oblique aerial photos acquired by a COTS(commercial off-the-shelf) UAV(unmanned aerial vehicle)(DJI INSPIRE-1) and 30 high-precision pre-deployed ground control points(GCPs) measured by FIFO A30 RTK(real-time kinematic). A sequence of 194 oblique photos were captured by UAV camera with 70° pitch angle following a dual-Z shaped flight route, which were in comparison with 74 orthophotos captured by a nadir-point UAV camera in single Z shaped flight route. The photos were imported into Photo Scan software for terrain construction along with POS(position and orientation) information. Firstly, a preliminary alignment of aerial photos was performed as well as a rough estimation of camera parameters. The RMS(root mean square) reprojection error of tie points was 0.808 pixel and the maximum reprojection error was 41.143 5 pixel. Secondly, the corresponding projections of GCPs were marked on each photo and a set of GCP references were established in Photo Scan. Thirdly, camera estimation was iteratively optimized with high precision GCP references until errors of GCPs and reprojection errors of tie points met desired standard. After 4 iterations, the GCP errors were stabilized and its reprojection error was down to 0.538 pixel, and the RMS reprojection error of tie points also decreased to 0.51 pixel and its maximum reprojection error was down to 7.8 pixel. Fourthly, based on the optimized camera parameters and original aerial photos, depth image of each photo was calculated and a dense gully point cloud model consisting of 9 537 948 points was built through PMVS(patch-based multi-view stereo) algorithm in Photo Scan. And fifthly, the 30 GCPs were classified into 2 categories; 10 GCPs that numbered multiples of three were selected as check points to evaluate the overall accuracy of gully model, while the others were used as geo-reference to the WGS-84 system. Finally, the georeferenced gully dense point cloud was rasterized to gully DEM(digital elevation model) and triangulated to gully TIN(triangulated irregular network) from which the gully DOM(digital ortho-photo map) was built. Afterwards, the accuracies of oblique photos derived gully point cloud model as well as DEM and DOM results were analyzed and compared with that of ortho-photos. Box plot of GCP errors verified the consistency between the X/Y/Z errors of check GCPs and reference GCPs in oblique photo result. Thus the constructed gully dense point cloud has a roughly 0.096 m planimetric RMSE and 0.018 m vertical RMSE, which fulfills the requirement of DLG(digital line graphic) and DOM in 1:500 scale(GH/Z 3003-2010). The achieved accuracy well meets the requirement of high precision gully modeling and monitoring. The result of ortho-photos has a 0.105 m planimetric RMSE and 0.036 m vertical RMSE. That confirms a 50% improvement of vertical accuracy with oblique photos. The overall density of the gully point cloud is 12 680 points/m2, which is comparative to terrestrial laser scanning, and it avoids the uneven sampling caused by multi-station assembling of TLS(terrestrial laser scanning). Except from a small patch of inner part in concave gully head, there aren't obvious holes in gully head or walls in the model. In addition, the area covered with vegetation can also be correctly constructed. On the contrary, there are open holes in concave gully head and vegetated area in ortho-photo result. Seen from the DEM and contour lines map of the gully, the constructed terrain model gives a fine description of gully morphology. However, there are burrs and breaks in contour line caused by vegetation outside the gully, which shows the necessity of vegetation removal. In general, the proposed method has advantages in high precision gully terrain modeling and shows great ability to further application.
In this paper, Sf M-MVS(structure from motion with multi-view stereo) method was introduced to construct a high precision terrain model of the typical gully on the Losses Plateau of northern Shaanxi in China, with oblique aerial photos acquired by a COTS(commercial off-the-shelf) UAV(unmanned aerial vehicle)(DJI INSPIRE-1) and 30 high-precision pre-deployed ground control points(GCPs) measured by FIFO A30 RTK(real-time kinematic). A sequence of 194 oblique photos were captured by UAV camera with 70° pitch angle following a dual-Z shaped flight route, which were in comparison with 74 orthophotos captured by a nadir-point UAV camera in single Z shaped flight route. The photos were imported into Photo Scan software for terrain construction along with POS(position and orientation) information. Firstly, a preliminary alignment of aerial photos was performed as well as a rough estimation of camera parameters. The RMS(root mean square) reprojection error of tie points was 0.808 pixel and the maximum reprojection error was 41.143 5 pixel. Secondly, the corresponding projections of GCPs were marked on each photo and a set of GCP references were established in Photo Scan. Thirdly, camera estimation was iteratively optimized with high precision GCP references until errors of GCPs and reprojection errors of tie points met desired standard. After 4 iterations, the GCP errors were stabilized and its reprojection error was down to 0.538 pixel, and the RMS reprojection error of tie points also decreased to 0.51 pixel and its maximum reprojection error was down to 7.8 pixel. Fourthly, based on the optimized camera parameters and original aerial photos, depth image of each photo was calculated and a dense gully point cloud model consisting of 9 537 948 points was built through PMVS(patch-based multi-view stereo) algorithm in Photo Scan. And fifthly, the 30 GCPs were classified into 2 categories; 10 GCPs that numbered multiples of three were selected as check points to evaluate the overall accuracy of gully model, while the others were used as geo-reference to the WGS-84 system. Finally, the georeferenced gully dense point cloud was rasterized to gully DEM(digital elevation model) and triangulated to gully TIN(triangulated irregular network) from which the gully DOM(digital ortho-photo map) was built. Afterwards, the accuracies of oblique photos derived gully point cloud model as well as DEM and DOM results were analyzed and compared with that of ortho-photos. Box plot of GCP errors verified the consistency between the X/Y/Z errors of check GCPs and reference GCPs in oblique photo result. Thus the constructed gully dense point cloud has a roughly 0.096 m planimetric RMSE and 0.018 m vertical RMSE, which fulfills the requirement of DLG(digital line graphic) and DOM in 1:500 scale(GH/Z 3003-2010). The achieved accuracy well meets the requirement of high precision gully modeling and monitoring. The result of ortho-photos has a 0.105 m planimetric RMSE and 0.036 m vertical RMSE. That confirms a 50% improvement of vertical accuracy with oblique photos. The overall density of the gully point cloud is 12 680 points/m2, which is comparative to terrestrial laser scanning, and it avoids the uneven sampling caused by multi-station assembling of TLS(terrestrial laser scanning). Except from a small patch of inner part in concave gully head, there aren't obvious holes in gully head or walls in the model. In addition, the area covered with vegetation can also be correctly constructed. On the contrary, there are open holes in concave gully head and vegetated area in ortho-photo result. Seen from the DEM and contour lines map of the gully, the constructed terrain model gives a fine description of gully morphology. However, there are burrs and breaks in contour line caused by vegetation outside the gully, which shows the necessity of vegetation removal. In general, the proposed method has advantages in high precision gully terrain modeling and shows great ability to further application.
引文
[1]郑粉莉,高学田.坡面土壤侵蚀过程研究进展[J].地理科学,2003,23(2):230-235.Zheng Fenli,Gao Xuetian.Research progresses in hillslope soil erosion Processes[J].Scientia Geographica Sinica,2003,23(2):230-235.(in Chinese with English abstract)
[2]李占斌,朱冰冰,李鹏.土壤侵蚀与水土保持研究进展[J].土壤学报,2008,45(5):802-809.Li Zhanbin,Zhu Bingbing,Li Peng.Advancement in study on soil erosion and soil and water conservation[J].Acta Pedologica Sinica,2008,45(5):802-809.(in Chinese with English abstract)
[3]Brasington J,Rumsby B T,Mcvey R A.Monitoring and modelling morphological change in a braided gravel-bed river using high resolution GPS-based survey[J].Earth Surface Processes and Landforms,2000,25(9):973-990.
[4]张鹏,郑粉莉,王彬,等.高精度GPS,三维激光扫描和测针板三种测量技术监测沟蚀过程的对比研究[J].水土保持通报,2008,28(5):11-15,20.Zhang Peng,Zheng Fenli,Wang Bin,et al.Comparative study of monitoring gully erosion morphology change process by using high precision GPS,Leica HDS 3000 laser scanner and needle board method[J].Bulletin of Soil and Water Conservation,2008,28(5):11-15,20.(in Chinese with English abstract).
[5]张岩,杨松,李镇,等.陕北黄土区水平条带整地措施对切沟发育的影响[J].农业工程学报,2015,31(7):125-130.Zhang Yan,Yang Song,Li Zhen,et al.Effect of narrow terrace on gully erosion in northern Shaanxi loess area[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(7):125-130.(in Chinese with English abstract)
[6]James L A,Watson D G,Hansen W F.Using Li DAR data to map gullies and headwater streams under forest canopy:South Carolina,USA[J].Catena,2007,71(1):132-144.
[7]李斌兵,黄磊,冯林,等.基于点云数据的切沟泥沙负载量不确定性研究[J].农业工程学报,2014,30(17):183-191.Li Binbing,Huang Lei,Feng Lin,et al.Uncertainty of gully sediment budgets based on laser point cloud data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(17):183-191.(in Chinese with English abstract)
[8]Martínez-casanoavs J A.A spatial information technology approach for the mapping and quantification of gully erosion[J].Catena,2003,50(2):293-308.
[9]李镇,张岩,杨松,等.Quick Bird影像目视解译法提取切沟形态参数的精度分析[J].农业工程学报,2014,30(20):179-186.Li Zhen,Zhang Yan,Yang Song,et al.Error assessment of extracting morphological parameters of bank gullies by manual visual interpretation based on Quick Bird imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(20):179-186.(in Chinese with English abstract)
[10]张岩,刘宪春,李智广,等.利用侵蚀模型普查黄土高原土壤侵蚀状况[J].农业工程学报,2012,28(10):165-171.Zhang Yan,Liu Xianchun,Li Zhiguang,et al.Surveying soil erosion condition in Loess Plateau using soil erosion model[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(10):165-171.(in Chinese with English abstract)
[11]李斌兵,黄磊.基于面向对象技术的黄土丘陵沟壑区切沟遥感提取方法研究[J].水土保持研究,2013,20(3):115-119,124.Li Binbing,Huang Lei.Study on recognition of the gully in loess hilly-gully region based on object-oriented technology.2013,20(3):115-119,124.(in Chinese with English abstract)
[12]李辉,余忠迪,蔡晓斌,等.基于无人机遥感的河流阶地提取[J].地球科学,2017,42(5):734-742.Li Hui,Yu Zhongdi,Cai Xiaobin,et al.River terrace extraction based on unmanned aerial vehicle remote sensing[J].Earth Science,2017,42(5):734-742.(in Chinese with English abstract)
[13]汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.Wang Xiaoqin,Wang Miaomiao,Wang Shaoqiang,et al.Extraction of vegetation information from visible unmanned aerial vehicle images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):152-159.(in Chinese with English abstract)
[14]Lowe D G.Distinctive image features from scale-invariant keys[J].International Journal of Computer Vision,2004,60(2):91-110.
[15]Snavely N,Seitz S M,Szeliski R.Modeling the world from internet photo collections[J].International Journal of Computer Vision,2008,80(2):189-210.
[16]Furukawa Y,Ponce J.Accurate,dense,and robust multiview stereopsis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.
[17]李俊利,李斌兵,柳方明,等.利用照片重建技术生成坡面侵蚀沟三维模型[J].农业工程学报,2015,31(1):125-132.Li Junli,Li Binbing,Liu Fangming,et al.Generating 3Dmodel of slope eroded gully based on photo reconstruction technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(1):125-132.(in Chinese with English abstract)
[18]Nex F,Remondino F.UAV for 3D mapping applications:Areview[J].Applied Geomatics,2014,6(1):1-15.
[19]Jirousek T,Kapica R,Vrublova D,et al.The testing of photoscan 3D object modelling software[J].Geodesy and Cartography,2014,40(2):68-74.
[20]Smith M W,Vericat D.From experimental plots to experimental landscapes:Topography,erosion and deposition in sub-humid badlands from structure-from-motion photogrammetry[J].Earth Surface Processes and Landforms,2015,40(12):1656-1671.
[21]Duarte L,Teodoro A C,Moutinho O,et al.Open-source GISapplication for UAV photogrammetry based on Mic Mac[J].International Journal of Remote Sensing,2017,38(8/10):3181-3202.
[22]魏占玉,Ramon A,何宏林,等.基于Sf M方法的高密度点云数据生成及精度分析[J].地震地质,2015,37(2):636-648.Wei Zhanyu,Ramon A,He Honglin,et al.Accuracy analysis of terrain point cloud acquired by"Structure from Motion"using aerial photos[J].Seismology and Geology,2015,37(2):636-648.(in Chinese with English abstract)
[23]Marzolff I,Poesen J.The potential of 3D gully monitoring with GIS using high-resolution aerial photography and a digital photogrammetry system[J].Geomorphology,2009,111(1):48-60.
[24]Peter K D,D’oleire-oltmanns S,Ries J B,et al.Soil erosion in gully catchments affected by land-levelling measures in the Souss basin,Morocco,analysed by rainfall simulation and UAV remote sensing data[J].Catena,2014,113(2):24-40.
[25]St?cker C,Eltner A,Karrasch P.Measuring gullies by synergetic application of UAV and close range photogrammetry:A case study from Andalusia,Spain[J].Catena,2015,132:1-11.
[26]Hansel P,Schindewolf M,Eltner,et al.Feasibility of high-resolution soil erosion measurements by means of rainfall simulations and Sf M Photogrammetry[J].Hydrology,2016,3(4):1-16.
[27]Nouwakpo S K,Weltz M A,Mcgwire K C.Assessing the performance of structure-from-motion photogrammetry and terrestrial Li DAR for reconstructing soil surface microtopography of naturally vegetated plots[J].Earth Surface Processes and Landforms,2016,41(3):308-322.
[28]Liu K,Ding H,Tang G,et al.Detection of catchment-scale gully-affected areas using unmanned aerial vehicle(UAV)on the Chinese Loess Plateau[J].ISPRS International Journal of Geo-Information,2016,5(12):238.
[29]Kaiser A,Neugirg F,Rock G,et al.Small-Scale Surface Reconstruction and Volume Calculation of Soil Erosion in Complex Moroccan Gully Morphology Using Structure from Motion[J].Remote Sensing,2014,6(8):7050-7080.
[30]Rossi P,Mancini F,Dubbini M,et al.Combining nadir and oblique UAV imagery to reconstruct quarry topography:methodology and feasibility analysis[J].European Journal of Remote Sensing,2017,50(1):211-221.
[31]国家测绘局.低空数字航空摄影测量内业规范:CH/Z 3003-2010[S].北京:测绘出版社,2010.
[2]李占斌,朱冰冰,李鹏.土壤侵蚀与水土保持研究进展[J].土壤学报,2008,45(5):802-809.Li Zhanbin,Zhu Bingbing,Li Peng.Advancement in study on soil erosion and soil and water conservation[J].Acta Pedologica Sinica,2008,45(5):802-809.(in Chinese with English abstract)
[3]Brasington J,Rumsby B T,Mcvey R A.Monitoring and modelling morphological change in a braided gravel-bed river using high resolution GPS-based survey[J].Earth Surface Processes and Landforms,2000,25(9):973-990.
[4]张鹏,郑粉莉,王彬,等.高精度GPS,三维激光扫描和测针板三种测量技术监测沟蚀过程的对比研究[J].水土保持通报,2008,28(5):11-15,20.Zhang Peng,Zheng Fenli,Wang Bin,et al.Comparative study of monitoring gully erosion morphology change process by using high precision GPS,Leica HDS 3000 laser scanner and needle board method[J].Bulletin of Soil and Water Conservation,2008,28(5):11-15,20.(in Chinese with English abstract).
[5]张岩,杨松,李镇,等.陕北黄土区水平条带整地措施对切沟发育的影响[J].农业工程学报,2015,31(7):125-130.Zhang Yan,Yang Song,Li Zhen,et al.Effect of narrow terrace on gully erosion in northern Shaanxi loess area[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(7):125-130.(in Chinese with English abstract)
[6]James L A,Watson D G,Hansen W F.Using Li DAR data to map gullies and headwater streams under forest canopy:South Carolina,USA[J].Catena,2007,71(1):132-144.
[7]李斌兵,黄磊,冯林,等.基于点云数据的切沟泥沙负载量不确定性研究[J].农业工程学报,2014,30(17):183-191.Li Binbing,Huang Lei,Feng Lin,et al.Uncertainty of gully sediment budgets based on laser point cloud data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(17):183-191.(in Chinese with English abstract)
[8]Martínez-casanoavs J A.A spatial information technology approach for the mapping and quantification of gully erosion[J].Catena,2003,50(2):293-308.
[9]李镇,张岩,杨松,等.Quick Bird影像目视解译法提取切沟形态参数的精度分析[J].农业工程学报,2014,30(20):179-186.Li Zhen,Zhang Yan,Yang Song,et al.Error assessment of extracting morphological parameters of bank gullies by manual visual interpretation based on Quick Bird imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(20):179-186.(in Chinese with English abstract)
[10]张岩,刘宪春,李智广,等.利用侵蚀模型普查黄土高原土壤侵蚀状况[J].农业工程学报,2012,28(10):165-171.Zhang Yan,Liu Xianchun,Li Zhiguang,et al.Surveying soil erosion condition in Loess Plateau using soil erosion model[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(10):165-171.(in Chinese with English abstract)
[11]李斌兵,黄磊.基于面向对象技术的黄土丘陵沟壑区切沟遥感提取方法研究[J].水土保持研究,2013,20(3):115-119,124.Li Binbing,Huang Lei.Study on recognition of the gully in loess hilly-gully region based on object-oriented technology.2013,20(3):115-119,124.(in Chinese with English abstract)
[12]李辉,余忠迪,蔡晓斌,等.基于无人机遥感的河流阶地提取[J].地球科学,2017,42(5):734-742.Li Hui,Yu Zhongdi,Cai Xiaobin,et al.River terrace extraction based on unmanned aerial vehicle remote sensing[J].Earth Science,2017,42(5):734-742.(in Chinese with English abstract)
[13]汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.Wang Xiaoqin,Wang Miaomiao,Wang Shaoqiang,et al.Extraction of vegetation information from visible unmanned aerial vehicle images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):152-159.(in Chinese with English abstract)
[14]Lowe D G.Distinctive image features from scale-invariant keys[J].International Journal of Computer Vision,2004,60(2):91-110.
[15]Snavely N,Seitz S M,Szeliski R.Modeling the world from internet photo collections[J].International Journal of Computer Vision,2008,80(2):189-210.
[16]Furukawa Y,Ponce J.Accurate,dense,and robust multiview stereopsis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.
[17]李俊利,李斌兵,柳方明,等.利用照片重建技术生成坡面侵蚀沟三维模型[J].农业工程学报,2015,31(1):125-132.Li Junli,Li Binbing,Liu Fangming,et al.Generating 3Dmodel of slope eroded gully based on photo reconstruction technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(1):125-132.(in Chinese with English abstract)
[18]Nex F,Remondino F.UAV for 3D mapping applications:Areview[J].Applied Geomatics,2014,6(1):1-15.
[19]Jirousek T,Kapica R,Vrublova D,et al.The testing of photoscan 3D object modelling software[J].Geodesy and Cartography,2014,40(2):68-74.
[20]Smith M W,Vericat D.From experimental plots to experimental landscapes:Topography,erosion and deposition in sub-humid badlands from structure-from-motion photogrammetry[J].Earth Surface Processes and Landforms,2015,40(12):1656-1671.
[21]Duarte L,Teodoro A C,Moutinho O,et al.Open-source GISapplication for UAV photogrammetry based on Mic Mac[J].International Journal of Remote Sensing,2017,38(8/10):3181-3202.
[22]魏占玉,Ramon A,何宏林,等.基于Sf M方法的高密度点云数据生成及精度分析[J].地震地质,2015,37(2):636-648.Wei Zhanyu,Ramon A,He Honglin,et al.Accuracy analysis of terrain point cloud acquired by"Structure from Motion"using aerial photos[J].Seismology and Geology,2015,37(2):636-648.(in Chinese with English abstract)
[23]Marzolff I,Poesen J.The potential of 3D gully monitoring with GIS using high-resolution aerial photography and a digital photogrammetry system[J].Geomorphology,2009,111(1):48-60.
[24]Peter K D,D’oleire-oltmanns S,Ries J B,et al.Soil erosion in gully catchments affected by land-levelling measures in the Souss basin,Morocco,analysed by rainfall simulation and UAV remote sensing data[J].Catena,2014,113(2):24-40.
[25]St?cker C,Eltner A,Karrasch P.Measuring gullies by synergetic application of UAV and close range photogrammetry:A case study from Andalusia,Spain[J].Catena,2015,132:1-11.
[26]Hansel P,Schindewolf M,Eltner,et al.Feasibility of high-resolution soil erosion measurements by means of rainfall simulations and Sf M Photogrammetry[J].Hydrology,2016,3(4):1-16.
[27]Nouwakpo S K,Weltz M A,Mcgwire K C.Assessing the performance of structure-from-motion photogrammetry and terrestrial Li DAR for reconstructing soil surface microtopography of naturally vegetated plots[J].Earth Surface Processes and Landforms,2016,41(3):308-322.
[28]Liu K,Ding H,Tang G,et al.Detection of catchment-scale gully-affected areas using unmanned aerial vehicle(UAV)on the Chinese Loess Plateau[J].ISPRS International Journal of Geo-Information,2016,5(12):238.
[29]Kaiser A,Neugirg F,Rock G,et al.Small-Scale Surface Reconstruction and Volume Calculation of Soil Erosion in Complex Moroccan Gully Morphology Using Structure from Motion[J].Remote Sensing,2014,6(8):7050-7080.
[30]Rossi P,Mancini F,Dubbini M,et al.Combining nadir and oblique UAV imagery to reconstruct quarry topography:methodology and feasibility analysis[J].European Journal of Remote Sensing,2017,50(1):211-221.
[31]国家测绘局.低空数字航空摄影测量内业规范:CH/Z 3003-2010[S].北京:测绘出版社,2010.