利用两级体素的光子点云自适应降噪
TP7%TN95; 光子计数激光雷达采用单光子探测器,受背景环境、目标特征和仪器性能等因素的影响,在记录目标散射/反射回波信号的同时还记录了大量的背景噪声.为实现海量光子点云中地物信号光子的高精准识别,本文提出利用两级体素的光子点云自适应降噪方法,包括:1)利用光子点云的空间分布特征构建大尺度的体素,结合体素的密度属性筛选包含密集信号光子点云的体素,实现光子点云的粗降噪;2)基于最近邻距离建立粗降噪后光子点云的小尺度体素,并利用拓扑关系进一步提取聚集于地物表面的信号光子.以Ice,Cloud and land Elevation Satellite-2/Advanced Topographic...
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| Published in | 红外与毫米波学报 Vol. 43; no. 6; pp. 832 - 845 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083
01.12.2024
上海海洋大学 信息学院,上海 201306%上海海洋大学 信息学院,上海 201306 国科大杭州高等研究院 物理与光电工程学院,浙江 杭州 310024 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-9014 |
| DOI | 10.11972/j.issn.1001-9014.2024.06.015 |
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| Abstract | TP7%TN95; 光子计数激光雷达采用单光子探测器,受背景环境、目标特征和仪器性能等因素的影响,在记录目标散射/反射回波信号的同时还记录了大量的背景噪声.为实现海量光子点云中地物信号光子的高精准识别,本文提出利用两级体素的光子点云自适应降噪方法,包括:1)利用光子点云的空间分布特征构建大尺度的体素,结合体素的密度属性筛选包含密集信号光子点云的体素,实现光子点云的粗降噪;2)基于最近邻距离建立粗降噪后光子点云的小尺度体素,并利用拓扑关系进一步提取聚集于地物表面的信号光子.以Ice,Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System(ICESat-2/ATLAS)获取白昼与夜晚光子点云的ATL03级数据为实验数据,将提出方法与改进Density-Based Spatial Clustering of Applications with Noise(DBSCAN)、改进Ordering points to identify the clustering structure(OPTICS)以及ATL08级数据产品进行比较分析.结果表明,该方法具有最优的性能表现,其平均精度(P)、召回率(R)和F1分数(F1)分别达到0.98、0.97和0.98. |
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| AbstractList | TP7%TN95; 光子计数激光雷达采用单光子探测器,受背景环境、目标特征和仪器性能等因素的影响,在记录目标散射/反射回波信号的同时还记录了大量的背景噪声.为实现海量光子点云中地物信号光子的高精准识别,本文提出利用两级体素的光子点云自适应降噪方法,包括:1)利用光子点云的空间分布特征构建大尺度的体素,结合体素的密度属性筛选包含密集信号光子点云的体素,实现光子点云的粗降噪;2)基于最近邻距离建立粗降噪后光子点云的小尺度体素,并利用拓扑关系进一步提取聚集于地物表面的信号光子.以Ice,Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System(ICESat-2/ATLAS)获取白昼与夜晚光子点云的ATL03级数据为实验数据,将提出方法与改进Density-Based Spatial Clustering of Applications with Noise(DBSCAN)、改进Ordering points to identify the clustering structure(OPTICS)以及ATL08级数据产品进行比较分析.结果表明,该方法具有最优的性能表现,其平均精度(P)、召回率(R)和F1分数(F1)分别达到0.98、0.97和0.98. |
| Abstract_FL | With a single-photon detector,photon-counting LiDAR(PCL)captures a large amount of background noise along with the target scattered/reflected echo signals,because of the influence of factors such as the background environ-ment,target characteristics,and instrument performance.To accurately extract the signal photons on the ground surface from a noisy photon point cloud(PPC),this paper presents an adaptive denoising approach for PPC using two levels of voxels.First,coarse denoising is performed utilizing large-scale voxels,which are built based on the spatial distribu-tion features of the PPC.The density of the voxel is then used to select the voxels that contained dense signal photons.Second,fine denoising with small-scale voxels is conducted.These voxels are built using the nearest neighbor distance,and a topologicalrelationship between voxels is used to further extract voxels containing signal photons aggregated on the ground surface.Finally,this method is performed on the PPC from ATL03 datasets collected by the Ice,Cloud,and Land Elevation Satellite-2 both during daytime and at night,and compared with the improved Density-Based Spatial Clustering of Applications with Noise(DBSCAN),improved Ordering Points to Identify the Clustering Structure(OP-TICS),and the method used in the ATL08 datasets.The results show that the proposed method has the best perfor-mance,with precision,recall,and F1 score of 0.98,0.97,and 0.98,respectively. |
| Author | 杨武钟 徐卫明 王振华 刘向锋 王凤香 舒嵘 |
| AuthorAffiliation | 上海海洋大学 信息学院,上海 201306%上海海洋大学 信息学院,上海 201306;中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083;国科大杭州高等研究院 物理与光电工程学院,浙江 杭州 310024 |
| AuthorAffiliation_xml | – name: 上海海洋大学 信息学院,上海 201306%上海海洋大学 信息学院,上海 201306;中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083%中国科学院上海技术物理研究所 中国科学院空间主动光电技术重点实验室,上海 200083;国科大杭州高等研究院 物理与光电工程学院,浙江 杭州 310024 |
| Author_FL | WANG Zhen-Hua LIU Xiang-Feng WANG Feng-Xiang YANG Wu-Zhong SHU Rong XU Wei-Ming |
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| Keywords | 光子计数激光雷达 denoising voxel 体素 photon counting LiDAR 光子点云 photon point cloud ICESat-2/ATLAS 降噪 |
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| Title | 利用两级体素的光子点云自适应降噪 |
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