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.

CLIMATIC RISK ZONING OF DOUBLE CROPPING SUPER RICE CULTIVATION IN HUNAN PROVINCE

Based on meteorological data including daily sunshine duration, temperature and precipitation from 97 meteorological stations in Hunan province during the period of 1981—2010, in combination with the field experiment in different places at different sowing dates, the precise climatic risk zoning of double cropping super rice cultivation has been studied by using the spatial interpolation method and other Geographical Information System(GIS)technologies. Three key climatic factors were selected including chilling in May, high temperature heat damage during July to early August and low temperature damage in autumn in this study. Furthermore, based on the analysis of climatic conditions suitable for double cropping super rice cultivation and climatic disasters, 8-22 ℃ active accumulated temperature, sunshine duration from late March to October, climatic risk index of the low temperature in autumn, and climatic risk index of chilling in May were selected as key climatic factors to study the precise agro-meteorological regionalization of double cropping super rice in Hunan province. The results showed that: the high-yielding zones of double cropping super rice in Hunan were mainly located in Zhuzhou, Hengyang, Yongzhou and Chenzhou City, the moderate-yielding zones were primarily located in the east and north reaches of Dongting Lake,together with most of Changsha, Zhuzhou and Xiangtan City, and other regions in Hunan were not suitable for double cropping super rice. These findings can provide valuable information for the large-scale cultivation of double cropping super rice in Hunan province.