干旱区生态环境知识资源中心(Arid): 基于K-means聚类与RBFNN的点云DEM构建方法

Arid

	基于K-means聚类与RBFNN的点云DEM构建方法
其他题名	Construction Method of Point Clouds'DEM Based on K-means Clustering and RBF Neural Network
	赵庆展 1; 李沛婷 1; 马永建 2; 田文忠 3
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2019
卷号	50 期号:9 页码:208-214
中文摘要	因无人机机载激光雷达( Light detection and ranging,LiDAR)数据具有离散性,在生成数字高程模型( Digital elevation model,DEM)时需选择有效插值方法。以荒漠植被区为研究背景,使用零-均值标准化方法归一化点云回波强度,利用肘方法确定最佳聚类数目,采用K-means方法对点云强度值聚类得到地面点云。在此基础上,采用克里金( Kriging)方法插值抽稀率为20%和80%的地面点云数据,且将点云高程作为变量,建立RBF神经网络预测模型,并通过线性回归检验方法对模型进行精度分析,采用Delaunay三角网内插生成高精度DEM。结果表明:采用K-means方法实现最佳聚类数目为4的聚类,得到地面点云48 722个,在点云较优抽稀率20%的情况下,径向基函数神经网络( Radical basis function neural network,RBFNN)训练时间为56 s,点云高程预测的决定系数R~2为0.887,均方根误差RMSE为0.168 m。说明使用RBFNN对K-means聚类滤波得到的地面点云进行高程预测效果较好,可为基于点云构建高精度DEM提供参考。
英文摘要	Digital elevation model ( DEM) is a basic surface information product for constructing hydrological models,drawing slope maps,and extracting topographic features and so on. Because unmanned aerial vehicle ( UAV) light detection and ranging ( LiDAR) point cloud data has discrete characteristics,a reasonable interpolation method needs to be selected when generating DEM based on point clouds. The desert vegetation area in Xinjiang was taken as the research background,the zero-mean normalization method was used to normalize the point clouds'echo intensity,the elbow method was used to determine the optimal number of clustering by K-means approach,and the K-means clustering method was used to cluster the point clouds'intensity values to obtain the test area's ground point clouds. After that,the Kriging interpolation method was used to interpolate the ground point clouds with the thinning rate of 20% and 80%,respectively. Furthermore,the point clouds'elevation value was used as a variable to establish the radical basis function neural network ( RBFNN) prediction model,the accuracy of RBFNN prediction model was analyzed by linear regression method,and then the high-precision DEM was generated by Delaunay triangulation interpolation. The results showed that K-means clustering method was adopted to realize the clustering with the optimal number of clustering as 4,and 48 722 ground point clouds were obtained. The root mean squared error ( RMSE) corresponding to the point cloud thinning rate of 20% was smaller,and RBFNN training time was 56 s when the point cloud thinning rate was 20%. The determination coefficient R~2 of fit for predicting the point clouds'elevation value was 0.887,and RMSE was 0.168 m when elevations of ground point clouds was predicted based on RBFNN. This method not only showed that the point cloud filtering can be realized by K-means clustering filtering, but also showed that the RBF neural network was a better way for predicting point cloud elevation. This can provide reference for constructing high-precision DEM based on point cloud.
中文关键词	无人机机载激光雷达 ; 数字高程模型 ; 肘方法 ; K-means聚类 ; 径向基函数神经网络 ; 线性回归
英文关键词	unmanned aerial vehicle light detection and ranging digital elevation model elbow method K-means clustering method radical basis function neural network linear regression
语种	中文
收录类别	CSCD
WOS类目	ENGINEERING ELECTRICAL ELECTRONIC
WOS研究方向	Engineering
CSCD记录号	CSCD:6566743
来源机构	石河子大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/316012
作者单位	1.石河子大学信息科学与技术学院;;国家遥感中心新疆兵团分部, ;;, 石河子;;石河子, ;; 832003;;832003; 2.兵团空间信息工程技术研究中心, 兵团空间信息工程技术研究中心, 石河子, 新疆 832003, 中国; 3.石河子大学机械电气工程学院, 石河子, 新疆 832003, 中国
推荐引用方式 GB/T 7714	赵庆展,李沛婷,马永建,等. 基于K-means聚类与RBFNN的点云DEM构建方法[J]. 石河子大学,2019,50(9):208-214.
APA	赵庆展,李沛婷,马永建,&田文忠.(2019).基于K-means聚类与RBFNN的点云DEM构建方法.农业机械学报,50(9),208-214.
MLA	赵庆展,et al."基于K-means聚类与RBFNN的点云DEM构建方法".农业机械学报 50.9(2019):208-214.