Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features

Abstract

This study proposes an accurate vegetation extraction method used for airborne laser scanning data of an urban plot based on point cloud neighborhood features to overcome the deficiencies in the current research on the precise extraction of vegetation in urban plots. First, the plane features in the R-neighborhood are combined with Euclidean distance clustering to extract the building point cloud accurately, and the rough vegetation point cloud is extracted using the discrete features in the R-neighborhood. Then, under the building point cloud constraints, combined with the Euclidean distance clustering method, the remaining building boundary points in the rough vegetation point cloud are removed. Finally, based on the vegetation point cloud after removing the building boundary point cloud, points within a specific radius r are extracted from the vegetation point cloud in the original data, and a complete urban plot vegetation extraction result is obtained. Two urban plots of airborne laser scanning data are selected to calculate the point cloud plane features and discrete features with R = 0.6 m and accurately extract the vegetation point cloud from the urban point cloud data. The visual effect and accuracy analysis results of vegetation extraction are compared under four different radius ranges of r = 0.5 m, r = 1 m, r = 1.5 m and r = 2 m. The best vegetation extraction results of the two plots are obtained for r = 1 m. The recall and precision are obtained as 92.19% and 98.74% for plot 1 and 94.30% and 98.73% for plot 2, respectively.