Airborne LiDAR Point Cloud Classification Based on Multiple-Entity Eigenvector Fusion

Point cloud classification is an important stage in the application of airborne LiDAR point cloud in urban modeling and road extraction. Although there arc many methods for point cloud classification, there arc still some problems such as multi-dimensional feature vector information redundancy and low accuracy of point cloud classification in complex scenes. To solve these problems, a point cloud classification method is proposed based on multi- entity eigenvector fusion. The method extracts the feature vectors based on point entity and object entity and classifies the point cloud data by using random forest combined with color information. The experimental results show that the proposed multi-entity classification method is more accurate than the single-entity classification method. In order to further analyze the validity of random forest for point cloud classification, the support vector machine (SVM) and the back propagation (BP) neural network arc used for a comparative analysis. The experimental results show that the three groups of point cloud classification results obtained by the random forest method arc higher than those by the other two methods in the recall rate and F1 score.