典型文献
A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering
文献摘要：
Clustering filtering is usually a practical method for light detection and ranging (LiDAR) point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing (ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10％.The results show that the proposed method is feasible and can be used in different terrains.
文献关键词：
中图分类号：
作者姓名：
Xingsheng Deng;Guo Tang;Qingyang Wang
作者机构：
School of Traffic and Transportation Engineering,Changsha University of Science & Technology,Changsha,410114,China
文献出处：
引用格式：
[1]Xingsheng Deng;Guo Tang;Qingyang Wang-.A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering)[J].大地测量与地球动力学（英文版）,2022(01):38-49
A类：
B类：
novel,fast,classification,filtering,LiDAR,point,clouds,small,grid,density,clustering,Clustering,usually,practical,method,light,detection,ranging,according,their,characteristic,attributes,However,amount,data,extremely,large,practice,making,impossible,directly,error,also,too,Moreover,many,existing,algorithms,have,poor,results,discontinuous,This,article,proposes,new,which,solve,problem,Based,spatial,features,ground,object,clustered,firstly,by,elevations,then,plane,selected,Thus,number,samples,dimensions,reduced,Using,DBSCAN,original,finally,divided,into,noise,experiment,uses,sets,provided,International,Society,Photogrammetry,Remote,Sensing,ISPRS,proposed,compared,other,eight,classical,Quantitative,qualitative,analysis,shows,that,has,good,applicability,urban,areas,rural,significantly,better,than,total,about,feasible,used,different,terrains
AB值：
0.463474
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
