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 ster...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。展开更多
The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtai...The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.展开更多
地面控制点GCP(Ground Control Point)是表达地理空间位置的信息数据,归结为空间位置坐标、点位局部影像、点位特征描述及说明(点之记)、辅助信息。本文基于"多级多源控制点影像数据库系统的建立研究"课题,重点阐述多级多源...地面控制点GCP(Ground Control Point)是表达地理空间位置的信息数据,归结为空间位置坐标、点位局部影像、点位特征描述及说明(点之记)、辅助信息。本文基于"多级多源控制点影像数据库系统的建立研究"课题,重点阐述多级多源控制点(GCP)的数据内容及形式、数据的获取方法、数据的组织方案及数据库的概念设计。展开更多
针对小面积的1∶500测图和三维建模项目需求,利用低成本的大疆P4R无人机作为航测平台,根据低成本卫星导航模块的数据质量特征,从数据预处理、解算模型、参数估计流程、模糊度固定方法、高精度基线解算五个方面进行动态后处理算法的优化...针对小面积的1∶500测图和三维建模项目需求,利用低成本的大疆P4R无人机作为航测平台,根据低成本卫星导航模块的数据质量特征,从数据预处理、解算模型、参数估计流程、模糊度固定方法、高精度基线解算五个方面进行动态后处理算法的优化设计,实现航拍影像的定位定姿系统(Position and Orientation System,POS)解算。实验结果表明:基于动态后处理定位算法能够满足无人机免像控1∶500大比例尺测图需求,软件已应用于国内外多个实际项目。展开更多
Nowadays,with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology,people have put forward higher requirements for the rapid prepr...Nowadays,with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology,people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images.The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images.In this study,the geometric correction accuracy under various geometric correction models(including affine transformation model,local triangulation model,polynomial model,direct linear transformation model,and rational function model)and resampling methods(including nearest neighbor resampling method,bilinear interpolation resampling method,and cubic convolution resampling method)were analyzed.Furthermore,the distribution,number,and accuracy of control points were analyzed based on the control variable method,and precise ground control points(GCPs)were analyzed.The results showed that the average geometric positioning error of UAV hyperspectral images(at 80 m altitude AGL)without geometric correction was as high as 3.4041 m(about 65 pixels).The optimal geometric correction model and parameter combination of the UAV hyperspectral image(at 80 m altitude AGL)used a local triangulation model,adopted a bilinear interpolation resampling method,and selected 12 edgemiddle distributed GCPs.The correction accuracy could reach 0.0493 m(less than one pixel).This study provides a reference for the geometric correction of UAV hyperspectral images.展开更多
文摘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。
基金supported by National Natural Science Foundation of China (Nos. 61272287, 61531014)the State Key Laboratory of Virtual Reality Technology and Systems (No. BUAA-VR-15KF-10)
文摘The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.
文摘地面控制点GCP(Ground Control Point)是表达地理空间位置的信息数据,归结为空间位置坐标、点位局部影像、点位特征描述及说明(点之记)、辅助信息。本文基于"多级多源控制点影像数据库系统的建立研究"课题,重点阐述多级多源控制点(GCP)的数据内容及形式、数据的获取方法、数据的组织方案及数据库的概念设计。
文摘针对小面积的1∶500测图和三维建模项目需求,利用低成本的大疆P4R无人机作为航测平台,根据低成本卫星导航模块的数据质量特征,从数据预处理、解算模型、参数估计流程、模糊度固定方法、高精度基线解算五个方面进行动态后处理算法的优化设计,实现航拍影像的定位定姿系统(Position and Orientation System,POS)解算。实验结果表明:基于动态后处理定位算法能够满足无人机免像控1∶500大比例尺测图需求,软件已应用于国内外多个实际项目。
基金financially supported by the National Nature Science Foundation of China(Grant No.32260388)the Major Scientific and Technological Projects of the XPCC(Grant No.2017DB005)the Technology Development Guided by the Central Government(Grant No.201610011).
文摘Nowadays,with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology,people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images.The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images.In this study,the geometric correction accuracy under various geometric correction models(including affine transformation model,local triangulation model,polynomial model,direct linear transformation model,and rational function model)and resampling methods(including nearest neighbor resampling method,bilinear interpolation resampling method,and cubic convolution resampling method)were analyzed.Furthermore,the distribution,number,and accuracy of control points were analyzed based on the control variable method,and precise ground control points(GCPs)were analyzed.The results showed that the average geometric positioning error of UAV hyperspectral images(at 80 m altitude AGL)without geometric correction was as high as 3.4041 m(about 65 pixels).The optimal geometric correction model and parameter combination of the UAV hyperspectral image(at 80 m altitude AGL)used a local triangulation model,adopted a bilinear interpolation resampling method,and selected 12 edgemiddle distributed GCPs.The correction accuracy could reach 0.0493 m(less than one pixel).This study provides a reference for the geometric correction of UAV hyperspectral images.
