Pole-shaped surface feature classification model based on vehicle-mounted LiDAR point cloud data
Pole-shaped surface features are important infrastructure in road scenes,and it is particularly important to study automated recognition and classification of these surface features,which can provide support for urban data updates and smart cities.This paper selected vehicle-mounted light detection and ranging(LiDAR)point cloud data as the research object and proposed an optimized bootstrap aggregating(Bagging)ensemble learning technique to address the limitations and challenges faced by current classification algorithms.Firstly,the paper filtered the original point cloud to obtain non-ground points.Secondly,the paper obtained the feature values of the pole-shaped surface feature point clouds and formed a feature vector,while constructing a machine learning classification model.Finally,experiments were conducted on pole-shaped surface feature classification by using a single classifier and the improved Bagging ensemble learning method.The experimental results show that the improved Bagging ensemble learning method based on multiple voting methods in this paper can effectively achieve the extraction and classification of pole-shaped surface features,with an accuracy of 98.47%.The research in this paper can provide certain references and guidance for point cloud data-based surface feature classification.
pole-shaped surface featurevehicle-mounted light detection and ranging(LiDAR)point cloudsupport vector machine(SVM)improve bootstrap aggregating(Bagging)ensembleclassification and extraction
万方数据
维普
