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.
关键词
杆状地物/车载激光雷达点云/支持向量机(SVM)/改进引导聚集(Bagging)集成/分类提取
Key words
pole-shaped surface feature/vehicle-mounted light detection and ranging(LiDAR)point cloud/support vector machine(SVM)/improve bootstrap aggregating(Bagging)ensemble/classification and extraction
维普
万方数据