A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering

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.

Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm

With the development of Chinese international trade,real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time,so that the hot zone information of a sea ship can be discovered in real-time.This technology has great research value for the future planning of maritime traffic.However,ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System(AIS)positioning system,and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering.This study proposes an adaptive time interval clustering algorithm based on density grid(called DAC-Stream).This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream,so that a ship’s hot zone information can be found efficiently and in real-time.Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid(called DC-Stream).