Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compens...Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compensation.Firstly,the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system,and the simulation result of scanning trajectory is consistent with experiments.The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified.Secondly,the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively,and an image scanning correction algorithm with controllable driving voltage compensation is obtained.The simulation experiment results of the proposed method show that the root-mean-square error(RMSE) and the corresponding curve between the scan value and the actual value at different distances,have a good correction effect for the pincushion distortion.Finally,the X-Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments,which further demonstrates the feasibility and practicability of our method.展开更多
Background： Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning （ALS） produces detail...Background： Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning （ALS） produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods： The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results： Power-law models were slightly less accurate than tree-centric models （RMSE 35% vs 32%） but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion： Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.展开更多
The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of air...The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.展开更多
A novel filtering algorithm for Lidar point clouds is presented, which can work well for complex cityscapes. Its main features are filtering based on raw Lidar point clouds without previous triangulation or rasterizat...A novel filtering algorithm for Lidar point clouds is presented, which can work well for complex cityscapes. Its main features are filtering based on raw Lidar point clouds without previous triangulation or rasterization. 3D topological relations among points are used to search edge points at the top of discontinuities, which are key information to recognize the bare earth points and building points. Experiment results show that the proposed algorithm can preserve discontinuous features in the bare earth and has no impact of size and shape of buildings.展开更多
Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable perfor...Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61775048 and 62027823)the Natural Science Foundation of Shenzhen(Grant No.JCYJ2020109150808037)。
文摘Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compensation.Firstly,the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system,and the simulation result of scanning trajectory is consistent with experiments.The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified.Secondly,the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively,and an image scanning correction algorithm with controllable driving voltage compensation is obtained.The simulation experiment results of the proposed method show that the root-mean-square error(RMSE) and the corresponding curve between the scan value and the actual value at different distances,have a good correction effect for the pincushion distortion.Finally,the X-Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments,which further demonstrates the feasibility and practicability of our method.
基金supported by Ministry of Business, Innovation and Employment core funding to Crown Research Institutes
文摘Background： Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning （ALS） produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods： The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results： Power-law models were slightly less accurate than tree-centric models （RMSE 35% vs 32%） but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion： Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.
基金Supported by the National Basic Research Program of China("973"Program)(2009CB72400401A)
文摘The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.
基金the National 863 Program of China (No.SQ2006AA12Z108506)
文摘A novel filtering algorithm for Lidar point clouds is presented, which can work well for complex cityscapes. Its main features are filtering based on raw Lidar point clouds without previous triangulation or rasterization. 3D topological relations among points are used to search edge points at the top of discontinuities, which are key information to recognize the bare earth points and building points. Experiment results show that the proposed algorithm can preserve discontinuous features in the bare earth and has no impact of size and shape of buildings.
基金National Natural Science Foundation of China(Nos.41971423,31972951,41771462)Hunan Provincial Natural Science Foundation of China(No.2020JJ3020)+1 种基金Science and Technology Planning Project of Hunan Province(No.2019RS2043,2019GK2132)Outstanding Youth Project of Education Department of Hunan Province(No.18B224)。
文摘Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.
