Adaptive Extraction of UAV Photogrammetric Point Cloud Road Surface
In UAV photogrammetry,traditional ground point cloud extraction methods have poor adaptability when extracting roads from image point cloud data.Therefore,this study proposes a UAV photogrammetric point cloud road adaptive extraction method.Firstly,the point cloud is divided into three categories based on its spatial geometric characteristics.Then,corresponding methods are applied to remove non-road point cloud categories.Finally,the point cloud data obtained through the adaptive extraction method is filtered for smoothing and subjected to color-based region growing segmentation.Experimental results show that the I-class error of road point cloud extracted by this method is 4.97%,and the II-class error is 1.14%.This method effectively extracts target road surfaces,improving the efficiency of point cloud data processing in UAV photogrammetric applications.
UAV photogrammetryimage point cloudpoint cloud filteringpoint cloud segmentationroad extraction
万方数据
维普
