为了提高城区机载激光雷达点云数据地物分类的分类精度,提出了一种基于Point-Net网络的多源融合点云地物分类方法。点云在地物三维特征表示上具有优势,而遥感影像包含丰富的光谱信息,因此设计了一种点云与遥感影像的配准融合方法,综合利用两种数据的优势。针对Point-Net网络存在缺少邻域信息的问题,提出一种针对融合点云数据的多尺度Point-Net分类模型,实现对融合点云数据的有效分类。利用城区点云数据验证本文算法,通过分析分类精度和分类时间对分类效果进行评价。结果证明:相比其他算法,本文算法有效提高了点云数据分类效果,实现了对城区点云数据的有效分类。

In order to improve the accuracy of object classification of point cloud data from airborne LiDAR, an object classification method for multi-source fusion point cloud data based on Point-Net is proposed. Point clouds can effectively represent three-dimensional features of objects, and remote-sensing images contain detailed spectral information. Therefore, a registration and fusion method for point cloud data and remote sensing images is designed to comprehensively utilize their advantages. Meanwhile, considering the lack of neighborhood information in Point-Net, a multi-scale Point-Net classification model for fusion point clouds is also proposed to realize effective classification of fusion point cloud data. The proposed algorithm is verified with point cloud data from urban regions and the classification effect is evaluated by analyzing the classification accuracy and time. Results show that, compared with other methods, the proposed method can effectively improve the classification accuracy of point cloud data, and achieve effective classification of point cloud data in urban areas.