ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

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 48722 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 56s when the point cloud thinning rate was 20%. The determination coefficient R2 of fit for predicting the point clouds’ elevation value was 0.887, and RMSE was 0.168m 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.