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A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering 被引量:3
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作者 Xingsheng Deng Guo Tang Qingyang Wang 《Geodesy and Geodynamics》 CSCD 2022年第1期38-49,共12页
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... 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. 展开更多
关键词 Small grid density clustering DBSCAN fast classification filtering algorithm
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FIFA-Fast Interpolation and Filtering Algorithm for Calculating Dyadic Green’s Function in the Electromagnetic Scattering of Multi-Layered Structures
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作者 Tiejun Yu Wei Cai 《Communications in Computational Physics》 SCIE 2006年第2期229-260,共32页
The dyadic Green’s function in multi-layer structures for Maxwell equations is a key component for the integral equation method,but time consuming to calculate.A novel algorithm,the Fast Interpolation and Filtering A... The dyadic Green’s function in multi-layer structures for Maxwell equations is a key component for the integral equation method,but time consuming to calculate.A novel algorithm,the Fast Interpolation and Filtering Algorithm(FIFA),for the calculation of the dyadic Green’s function in multi-layer structures is proposed in this paper.We discuss in specific details,ready for use in practical calculations of scattering in layer media,how to apply FIFA to calculate various components of the dyadic Green’s function.The algorithm is based on two techniques:interpolation of Green’s function both in the spectral domain and spatial domain,and low pass filter window based acceleration.Compared to the popular Complex Image Method(CIM),FIFA provides the same speed and overcomes several difficulties associated with CIM while being more general and robust.Specifically,there are no limitations on the frequency range,the number of layers in the structure and the type of Green’s functions to be calculated,and moreover,no need to extract surface wave poles from the spectral form of the Green’s function.Numerical results are given to demonstrate the efficiency and robustness of the proposed method. 展开更多
关键词 fast interpolation and filtering algorithm(FIFA) complex image method(CIM) low pass filter window(LPFW) interpolation table(IT) electromagnetic(EM)
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