激光与光电子学进展, 2019, 56 (5): 052804, 网络出版: 2019-07-31   

基于多尺度特征和PointNet的LiDAR点云地物分类方法 下载: 1798次

Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet
作者单位
1 空军工程大学信息与导航学院, 陕西 西安 710077
2 东北电力大学理学院, 吉林 吉林 132000
摘要
针对复杂场景下激光雷达测量(LiDAR)点云数据的地物分类问题,提出了一种基于多尺度特征和PointNet的深度神经网络模型,该方法改进了PointNet提取局部特征的能力,实现了复杂场景下LiDAR点云的自动分类。在PointNet网络基础上添加多尺度网络提取点的局部特征,将不同尺度点的局部特征通过全连接层组成一个多维特征,并与PointNet提取的全局特征相结合,返回每个点类的分数以完成点云分类标签。利用Semantic 三维数据集和ISPRS提供的Vaihingen数据集,验证了所提深度神经网络模型。研究结果表明,与其他用于点云分类的神经网络相比,所提算法达到了更高的分类精度。
Abstract
For the terrain classification problem of light detection and ranging (LiDAR) point cloud data in complex scenes, a deep neural network model based on multi-scale features and PointNet is proposed. The method improves the ability of PointNet to extract local features and realizes the automatic classification of LiDAR point cloud under complex scenes. Multi-scale network on the basis of PointNet network is added to extract the local features of points, and the local features of different scale points are formed into a multi-dimensional feature through the full connection layer, and combined with the global features extracted by PointNet, the score of each point class is returned to complete the point cloud classification label. The proposed deep neural network model is verified by using the Semantic three-dimensional dataset and the Vaihingen dataset provided by ISPRS. The research results show that the proposed algorithm achieves higher classification accuracy compared with other neural networks for point cloud classification.

赵中阳, 程英蕾, 释小松, 秦先祥, 李鑫. 基于多尺度特征和PointNet的LiDAR点云地物分类方法[J]. 激光与光电子学进展, 2019, 56(5): 052804. Zhongyang Zhao, Yinglei Cheng, Xiaosong Shi, Xianxiang Qin, Xin Li. Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet[J]. Laser & Optoelectronics Progress, 2019, 56(5): 052804.

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