A novel three-dimensional（3D） imaging lidar system which is based on a virtual instrument technique is introduced in this paper. The main characteristics of the system include： the capability of modeling a 3D objec...A novel three-dimensional（3D） imaging lidar system which is based on a virtual instrument technique is introduced in this paper. The main characteristics of the system include： the capability of modeling a 3D object in accordance with the actual one by connecting to a geographic information system（GIS）, and building the scene for the lidar experiment including the simulation environment. The simulation environment consists of four parts： laser pulse, atmospheric transport,target interaction, and receiving unit. Besides, the system provides an interface for the on-site experiment. In order to process the full waveform, we adopt the combination of pulse accumulation and wavelet denoising for signal enhancement.We also propose an optimized algorithm for data decomposition： the V-L decomposition method, which combines Vondrak smoothing and laser-template based fitting. Compared with conventional Gaussian decomposition, the new method brings an improvement in both precision and resolution of data decomposition. After applying V-L decomposition to the lidar system, we present the 3D reconstructed model to demonstrate the decomposition method.展开更多
We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak ...We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak width and backscatter cross section)are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets,which contribute to the recognition and classification of various scatterers.The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar,and it can obtain more specific target information compared to photon-counting discrete-point lidar,which can provide a potential alternative technique for tomographic surveying and mapping.展开更多
山体滑坡会导致生命和财产损失,获取完整的滑坡空间分布图及对易发区域的准确判定有利于指导生产、生活和生态空间优化。在滑坡调查过程中,茂密的植被覆盖使滑坡调查难度加大,机载激光雷达(light detection and ranging,LiDAR)技术的穿...山体滑坡会导致生命和财产损失,获取完整的滑坡空间分布图及对易发区域的准确判定有利于指导生产、生活和生态空间优化。在滑坡调查过程中,茂密的植被覆盖使滑坡调查难度加大,机载激光雷达(light detection and ranging,LiDAR)技术的穿透能力使真实地形特征得以呈现,从而实现植被茂密区滑坡识别。该文通过仿地飞行获取研究区LiDAR点云数据,基于点云数据得到数字高程模型(digital elevation model,DEM),在山体阴影分析、彩色增强显示及三维场景模拟基础上,识别出区域内已有滑坡的位置与规模,经野外核实,滑坡解译精度为86.4%。针对滑坡易发区评价问题,以现有滑坡为样本,首次采用遥感分类思维开展滑坡易发区划定,采用小区域内与滑坡发育有关的高程、坡度和地表起伏度组合成影像,以支持向量机为分类方法,判定出滑坡易发区域,经滑坡检验样本分析,滑坡识别精度为81.91%。研究表明:基于高精度的LiDAR数据及其视觉增强后的图像能识别小型滑坡,采用支持向量机分类法可以准确确定滑坡易发区,为下一步三生空间规划与优化提供依据。展开更多
Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light...Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.展开更多
针对现有基于伪点云的3D目标检测算法精度远低于基于真实激光雷达(Light Detection and ranging,LiDar)点云的3D目标检测,本文研究伪点云重构,并提出适合伪点云的3D目标检测网络.考虑到由图像深度转换得到的伪点云稠密且随深度增大逐渐...针对现有基于伪点云的3D目标检测算法精度远低于基于真实激光雷达(Light Detection and ranging,LiDar)点云的3D目标检测,本文研究伪点云重构,并提出适合伪点云的3D目标检测网络.考虑到由图像深度转换得到的伪点云稠密且随深度增大逐渐稀疏,本文提出深度相关伪点云稀疏化方法,在减少后续计算量的同时保留中远距离更多的有效伪点云,实现伪点云重构.本文提出LiDar点云指导下特征分布趋同与语义关联的3D目标检测网络,在网络训练时引入LiDar点云分支来指导伪点云目标特征的生成,使生成的伪点云特征分布趋同于LiDar点云特征分布,从而降低数据源不一致造成的检测性能损失;针对RPN(Region Proposal Network)网络获取的3D候选框内的伪点云间语义关联不足的问题,设计注意力感知模块,在伪点云特征表示中通过注意力机制嵌入点间的语义关联关系,提升3D目标检测精度.在KITTI 3D目标检测数据集上的实验结果表明:现有的3D目标检测网络采用重构后的伪点云,检测精度提升了2.61%;提出的特征分布趋同与语义关联的3D目标检测网络,将基于伪点云的3D目标检测精度再提升0.57%,相比其他优秀的3D目标检测方法在检测精度上也有提升.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.608320036)
文摘A novel three-dimensional（3D） imaging lidar system which is based on a virtual instrument technique is introduced in this paper. The main characteristics of the system include： the capability of modeling a 3D object in accordance with the actual one by connecting to a geographic information system（GIS）, and building the scene for the lidar experiment including the simulation environment. The simulation environment consists of four parts： laser pulse, atmospheric transport,target interaction, and receiving unit. Besides, the system provides an interface for the on-site experiment. In order to process the full waveform, we adopt the combination of pulse accumulation and wavelet denoising for signal enhancement.We also propose an optimized algorithm for data decomposition： the V-L decomposition method, which combines Vondrak smoothing and laser-template based fitting. Compared with conventional Gaussian decomposition, the new method brings an improvement in both precision and resolution of data decomposition. After applying V-L decomposition to the lidar system, we present the 3D reconstructed model to demonstrate the decomposition method.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11774095,11804099 and 11621404the Shanghai Basic Research Project under Grant No 18JC1412200the Program of Introducing Talents of Discipline to Universities under Grant No B12024
文摘We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak width and backscatter cross section)are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets,which contribute to the recognition and classification of various scatterers.The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar,and it can obtain more specific target information compared to photon-counting discrete-point lidar,which can provide a potential alternative technique for tomographic surveying and mapping.
基金funded by the Guangxi Natural Science Fund for Innovation Research Team (No. 2019JJF50001)the Natural Science Foundation of Fujian Province,China (No. 2019 J01396)+1 种基金the Special Fund for Guangxi Innovation and Driving Development (Major science and technology projects)(No. 2018AA13005)the Youth Innovation Promotion Association CAS (2019130)。
文摘Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.
