As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the lim...As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the limitations of traditional point cloud segmentation methods,which struggle to accurately extract structural parameters and capture the spatial layout of individual rubber trees.Therefore,we propose an optimized dual-channel clustering method for the UAV LiDAR-based Rubber Tree Point Cloud Segmentation Network(RsegNet)for improved assessment of rubber tree architecture and traits.Firstly,we designed a cosine feature extraction network,termed CosineU-Net,to address the branch-and-leaf overlap problem by calculating the cosine similarity of the spatial and positional features of each point,leveraging deep learning approaches to improve feature representation.Secondly,we constructed a dual-channel clustering module reducing prediction error in rubber tree point cloud data,integrating multi-class association and background classification to tackle background interference.The cluster identification and separation accuracy in high-dimensional data processing is enhanced through a dy-namic clustering optimization algorithm.In our self-built dataset and across five regions of the FOR-instance forest dataset,RsegNet achieved the best performance compared to five state-of-the-art networks,reaching an F-score of 86.1%.This method calculated structural attributes including height,crown diameter,and volume for rubber trees in three areas under different environments in Danzhou City,Hainan Province,providing robust support for precise monitoring,plantation management,and health assessment.展开更多
针对目前城市道路场景中行道树提取方法需要设置的参数较多以及树冠点云相互重叠难以精确分割的问题,文章采用一种行道树提取与单株木分割算法。首先通过布料滤波算法从原始点云中移除地面点,并利用半径滤波滤除离群点,去除地面点和噪...针对目前城市道路场景中行道树提取方法需要设置的参数较多以及树冠点云相互重叠难以精确分割的问题,文章采用一种行道树提取与单株木分割算法。首先通过布料滤波算法从原始点云中移除地面点,并利用半径滤波滤除离群点,去除地面点和噪声点对行道树提取的影响;然后通过增加PointNet++网络的点集抽象模块(set abstraction,SA)提高模型特征提取能力,使模型更适用于行道树点云的提取,并利用改进后的网络从原始点云中提取行道树点云;最后结合密度聚类算法(density-based spatial clustering of applications with noise,DBSCAN)与K-Means算法对相互重叠的行道树点云进行分割,得到单株木信息。为验证该方法的有效性,以北京永昌路道路数据集进行训练测试。结果表明:改进后模型的行道树点云平均提取精度和交并比(intersection over union,IoU)分别提高了9.2%和15.1%,达到了94.5%、0.916;单木分割平均精度达到了91.3%。展开更多
Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural infor...Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information.However,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult.In this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was proposed.In coarse registration,point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional(2D)images,which are used to estimate the initial positions of multiple scans.Corresponding feature-point pairs are then extracted from these series of 2D images.In fine registration,point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters.To evaluate the accuracy of registration results,we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans.For accurate evaluation,we conducted experiments on two simulated trees and six real-world trees.Average registration errors of the proposed method were 0.026 m around on simulated tree point clouds,and 0.049 m around on real-world tree point clouds.展开更多
单木分割在森林结构分析、林木参数提取以及森林生物量反演中具有重要作用。激光雷达(Light Detection and Ranging,LiDAR)作为一种低成本、高效率的数据源,为森林单木分割研究提供了坚实的数据基础。目前的单木分割研究主要集中在结构...单木分割在森林结构分析、林木参数提取以及森林生物量反演中具有重要作用。激光雷达(Light Detection and Ranging,LiDAR)作为一种低成本、高效率的数据源,为森林单木分割研究提供了坚实的数据基础。目前的单木分割研究主要集中在结构较为简单的森林区域,通常通过考虑点云之间的空间关系,制定合适的判别准则来实现单木的分割。然而,针对结构复杂的森林,现有的单木分割算法研究相对较少。提出了一种融合核密度估计、数字表面模型和K-means聚类等方法的单木分割算法。研究结果表明:以甘肃省甘南藏族自治区为研究区,对西北云杉林进行单木分割时,该方法能够显著提高人工云杉林与天然云杉林的分割精度。与传统的K-means聚类单木分割算法相比,该方法的整体棵数查全率分别提高了32%和15%,查准率分别提高了51%和27%,分别达到了83%和89%的查全率,以及92%和55%的查准率。这一方法为机载LiDAR在森林生态应用中的进一步应用提供了新的技术支持,特别为复杂林型结构中的单木分割问题提供了一种高效、简便的解决方案。展开更多
基金This work was supported by the Hainan Province Science and Technology Special Fund(Grant No.ZDYF2025XDNY113)the Central Public-interest Scientific Institution Basal Research Fund(Grant No.1630032022007)+2 种基金the Special Fund for Hainan Excellent Team"Rubber Genetics and Breeding"(Grant No.20210203)the Hunan Provincial Natural Science Foundation Project(Grant No.2025JJ50385)in part by the National Natural Science Foundation of China(Grant No.62276276).
文摘As an important tropical cash crop,rubber trees play a key role in the rubber industry and ecosystem.However,a significant challenge in precision agriculture and refined management of rubber plantation lies in the limitations of traditional point cloud segmentation methods,which struggle to accurately extract structural parameters and capture the spatial layout of individual rubber trees.Therefore,we propose an optimized dual-channel clustering method for the UAV LiDAR-based Rubber Tree Point Cloud Segmentation Network(RsegNet)for improved assessment of rubber tree architecture and traits.Firstly,we designed a cosine feature extraction network,termed CosineU-Net,to address the branch-and-leaf overlap problem by calculating the cosine similarity of the spatial and positional features of each point,leveraging deep learning approaches to improve feature representation.Secondly,we constructed a dual-channel clustering module reducing prediction error in rubber tree point cloud data,integrating multi-class association and background classification to tackle background interference.The cluster identification and separation accuracy in high-dimensional data processing is enhanced through a dy-namic clustering optimization algorithm.In our self-built dataset and across five regions of the FOR-instance forest dataset,RsegNet achieved the best performance compared to five state-of-the-art networks,reaching an F-score of 86.1%.This method calculated structural attributes including height,crown diameter,and volume for rubber trees in three areas under different environments in Danzhou City,Hainan Province,providing robust support for precise monitoring,plantation management,and health assessment.
文摘针对目前城市道路场景中行道树提取方法需要设置的参数较多以及树冠点云相互重叠难以精确分割的问题,文章采用一种行道树提取与单株木分割算法。首先通过布料滤波算法从原始点云中移除地面点,并利用半径滤波滤除离群点,去除地面点和噪声点对行道树提取的影响;然后通过增加PointNet++网络的点集抽象模块(set abstraction,SA)提高模型特征提取能力,使模型更适用于行道树点云的提取,并利用改进后的网络从原始点云中提取行道树点云;最后结合密度聚类算法(density-based spatial clustering of applications with noise,DBSCAN)与K-Means算法对相互重叠的行道树点云进行分割,得到单株木信息。为验证该方法的有效性,以北京永昌路道路数据集进行训练测试。结果表明:改进后模型的行道树点云平均提取精度和交并比(intersection over union,IoU)分别提高了9.2%和15.1%,达到了94.5%、0.916;单木分割平均精度达到了91.3%。
基金funded by the Fundamental Research Funds for the Central Universities(No.2021ZY92)National Students'innovation and entrepreneurship training program(No.201710022076)the State Scholarship Fund from China Scholarship Council(CSC No.201806515050).
文摘Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information.However,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult.In this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was proposed.In coarse registration,point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional(2D)images,which are used to estimate the initial positions of multiple scans.Corresponding feature-point pairs are then extracted from these series of 2D images.In fine registration,point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters.To evaluate the accuracy of registration results,we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans.For accurate evaluation,we conducted experiments on two simulated trees and six real-world trees.Average registration errors of the proposed method were 0.026 m around on simulated tree point clouds,and 0.049 m around on real-world tree point clouds.
文摘单木分割在森林结构分析、林木参数提取以及森林生物量反演中具有重要作用。激光雷达(Light Detection and Ranging,LiDAR)作为一种低成本、高效率的数据源,为森林单木分割研究提供了坚实的数据基础。目前的单木分割研究主要集中在结构较为简单的森林区域,通常通过考虑点云之间的空间关系,制定合适的判别准则来实现单木的分割。然而,针对结构复杂的森林,现有的单木分割算法研究相对较少。提出了一种融合核密度估计、数字表面模型和K-means聚类等方法的单木分割算法。研究结果表明:以甘肃省甘南藏族自治区为研究区,对西北云杉林进行单木分割时,该方法能够显著提高人工云杉林与天然云杉林的分割精度。与传统的K-means聚类单木分割算法相比,该方法的整体棵数查全率分别提高了32%和15%,查准率分别提高了51%和27%,分别达到了83%和89%的查全率,以及92%和55%的查准率。这一方法为机载LiDAR在森林生态应用中的进一步应用提供了新的技术支持,特别为复杂林型结构中的单木分割问题提供了一种高效、简便的解决方案。
