 Object-based point cloud analysis, OBPA, is a powerful tool for extracting information from airborne LiDAR data. We propose a novel approach for classifying urban point clouds using a combination of geometric, radiometric, topological, and echo characteristics. The surface growing algorithm is used to cluster the point clouds without outliers, while 13 features are calculated to provide a more accurate classification. Support vector machines, SVMs, are then used to classify the segments and connected component analysis, CCA, is applied to optimize the results. Experimental results show that the proposed OBPA method achieves high accuracy on three datasets with varying point densities, demonstrating its effectiveness in urban point cloud classification. This article was authored by Xiao Jiangning, Xiang Guolin, and Ji Xianzhang.