Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically...Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.展开更多
[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using L...[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.展开更多
The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a rese...The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.展开更多
The wearing course conditions strongly affect road pavements quality in terms of traffic safety and overall functionality.Surface texture can be considered a very strategic aspect to assess road pavement status,in ord...The wearing course conditions strongly affect road pavements quality in terms of traffic safety and overall functionality.Surface texture can be considered a very strategic aspect to assess road pavement status,in order to predict its degradation and to define an effective maintenance program.Nowadays,common texture assessment approaches are mainly empirical and based on in-situ and/or laboratory direct measurements,thus the quantity and quality of the obtainable information are limited.On the other hand,advanced contactless techniques require expensive and often complicated equipment that can be hardly used in common applications.In this regard,a low budget close-range photogrammetry technique for road pavements 3D surface texture analysis is here proposed.14 areal texture parameters including depth,volume,distribution and feature indicators have been determined by analysing the 3D models.The outcomes have been compared with those found with the traditional volumetric patch and pendulum tests,and a complete pairwise correlation matrix has been obtained.Volume patch test exhibits a high relationship with different volume and height surface texture parameters,while lowcorrelations have been found comparing pendulum test with the intrinsic and statistical indicators.The results and their relationships have been commented in-depth along with proposed further research activities.展开更多
This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to me...This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to measure the shape and deformation of the skin at the biceps brachii of a volunteer in vivo during arm wrestling.We observed the banding phenomenon of arm skin strain during muscle contraction and developed a model to evaluate the moment provided by the biceps brachii.According to this model,the strain field of the area of interest on the skin was measured,and the forearm angles most favorable and unfavorable to the work of the biceps brachii were analyzed.This study demonstrates the considerable potential of applying DIC and its extension method to the in vivo measurement of human skin and facilitates the use of the in vivo measurement of skin deformation in various sports in the future.展开更多
Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that res...Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.展开更多
Close-range photogrammetry is to determine the shape and size of the object, instead of it's absolute position. Therefore, at first, any translation and rotation of the photogrammetric model of the object caused b...Close-range photogrammetry is to determine the shape and size of the object, instead of it's absolute position. Therefore, at first, any translation and rotation of the photogrammetric model of the object caused by whole geodesic, photographic and photogrammetric procedures in close-range photogrammetry could not be considered. However, it is necessary to analyze all the reasons which cause the deformations of the shape and size and to present their corresponding theories and equations. This situation, of course, is very different from the conventional topophotogrammetry. In this paper some specific characters of limit errors in close-range photogrammetry are presented in detail, including limit errors for calibration of interior elements for close-range cameras, the limit errors of relative and absolute orientations in close-range cameras, the limit errors of relative and absolute orientations in close-range photogrammetric procedures, and the limit errors of control works in close-range photogrammetry. A theoretical equation of calibration accuracy for close-range camerais given. Relating to the three examples in this paper, their theoretical accuracy requirement of interior elements of camera change in the scope of ±(0.005–0.350) mm. This discussion permits us to reduce accuracy requirement in calibration for an object with small relief, but the camera platform is located in violent vibration environment. Another theoretical equation of relative RMS of base lines (m S/S) and the equation RMS of start direction are also presented. It is proved that them S/S could be equal to the relative RMS ofm ΔX/ΔX. It is also proved that the permitting RMS of start direction is much bigger than the traditionally used one. Some useful equations of limit errors in close-range photogrammetry are presented as well. Suggestions mentioned above are perhaps beneficial for increasing efficiency, for reducing production cost.展开更多
可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。...可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。利用卫星获取的前、后视CCD影像,并在其上添加由计算机生成的高层目标(约300m)的图像,验证了生成的正直影像对立体测绘的可行性,试验的高层目标点的坐标量测中误差为实验影像的0.5像元之内。展开更多
基金partially funded by the International Foundation for Science(Grant No:I-1-D-6066-1).
文摘Stem volume estimation is crucial in forest ecology and management,particularly for timber harvesting strategies and carbon stock assessments.This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry(CRP)data and to evaluate its performance against conventional volume equations for stem volume estimation.A dataset of 30 trees across five dominant savanna species was used to fit the taper model,which was validated using a separate dataset of 322 trees from 14 species.The results demonstrated significant improvements in volume estimation accuracy when using the taper equation.At the tree level,the root mean square error(RMSE)decreased by 47%,from 598 to 319 dm^(3),and the mean absolute bias(MAB)by 48%,from 328 to 172 dm3,compared to volume equations.Similarly,at the plot level,RMSE was reduced by 42% and MAB by 40%.The model performed well for species with regular forms.However,species with irregular tapers exhibited higher errors,reflecting the challenges of modeling stem forms of mixed species.The use of CRP proved valuable,providing high-resolution diameter measurements that improved model parameterization.This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms.The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems,contributing to better forest management and resource monitoring practices.
基金Supported by National Natural Science Foundation of China(30770401)National Eleventh Five-Year Plan for Forestry Scienceand Technology Support Topics(2006BADO3A0505)~~
文摘[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.
基金This research was funded by the National Natural Science Foundation of China[grant number 41971350 and 41571437]Beijing Advanced Innovation Centre for Future Urban Design Project[grant number UDC2019031724]+4 种基金Teacher Support Program for Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture[grant number JDJQ20200307]State Key Laboratory of Geo-Information Engineering[grant number SKLGIE2019-Z-3-1]Open Research Fund Program of LIESMARS[grant number 19E01]National Key Research and Development Program of China[grant number 2019YFC1520100]The Fundamental Research Funds for Beijing University of Civil Engineering and Architecture[grant number X18050].
文摘The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.
文摘The wearing course conditions strongly affect road pavements quality in terms of traffic safety and overall functionality.Surface texture can be considered a very strategic aspect to assess road pavement status,in order to predict its degradation and to define an effective maintenance program.Nowadays,common texture assessment approaches are mainly empirical and based on in-situ and/or laboratory direct measurements,thus the quantity and quality of the obtainable information are limited.On the other hand,advanced contactless techniques require expensive and often complicated equipment that can be hardly used in common applications.In this regard,a low budget close-range photogrammetry technique for road pavements 3D surface texture analysis is here proposed.14 areal texture parameters including depth,volume,distribution and feature indicators have been determined by analysing the 3D models.The outcomes have been compared with those found with the traditional volumetric patch and pendulum tests,and a complete pairwise correlation matrix has been obtained.Volume patch test exhibits a high relationship with different volume and height surface texture parameters,while lowcorrelations have been found comparing pendulum test with the intrinsic and statistical indicators.The results and their relationships have been commented in-depth along with proposed further research activities.
基金This study was supported by the National Natural Science Foun-dation of China(NSFC)(No.11902074).
文摘This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to measure the shape and deformation of the skin at the biceps brachii of a volunteer in vivo during arm wrestling.We observed the banding phenomenon of arm skin strain during muscle contraction and developed a model to evaluate the moment provided by the biceps brachii.According to this model,the strain field of the area of interest on the skin was measured,and the forearm angles most favorable and unfavorable to the work of the biceps brachii were analyzed.This study demonstrates the considerable potential of applying DIC and its extension method to the in vivo measurement of human skin and facilitates the use of the in vivo measurement of skin deformation in various sports in the future.
基金National Natural Science Foundation of China(41471288).
文摘Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.
文摘Close-range photogrammetry is to determine the shape and size of the object, instead of it's absolute position. Therefore, at first, any translation and rotation of the photogrammetric model of the object caused by whole geodesic, photographic and photogrammetric procedures in close-range photogrammetry could not be considered. However, it is necessary to analyze all the reasons which cause the deformations of the shape and size and to present their corresponding theories and equations. This situation, of course, is very different from the conventional topophotogrammetry. In this paper some specific characters of limit errors in close-range photogrammetry are presented in detail, including limit errors for calibration of interior elements for close-range cameras, the limit errors of relative and absolute orientations in close-range cameras, the limit errors of relative and absolute orientations in close-range photogrammetric procedures, and the limit errors of control works in close-range photogrammetry. A theoretical equation of calibration accuracy for close-range camerais given. Relating to the three examples in this paper, their theoretical accuracy requirement of interior elements of camera change in the scope of ±(0.005–0.350) mm. This discussion permits us to reduce accuracy requirement in calibration for an object with small relief, but the camera platform is located in violent vibration environment. Another theoretical equation of relative RMS of base lines (m S/S) and the equation RMS of start direction are also presented. It is proved that them S/S could be equal to the relative RMS ofm ΔX/ΔX. It is also proved that the permitting RMS of start direction is much bigger than the traditionally used one. Some useful equations of limit errors in close-range photogrammetry are presented as well. Suggestions mentioned above are perhaps beneficial for increasing efficiency, for reducing production cost.
文摘可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。利用卫星获取的前、后视CCD影像,并在其上添加由计算机生成的高层目标(约300m)的图像,验证了生成的正直影像对立体测绘的可行性,试验的高层目标点的坐标量测中误差为实验影像的0.5像元之内。
