基于LiDAR点云的建筑物分割深度学习模型研究

doi:10.13474/j.cnki.11-2246.2021.379

测绘通报 ›› 2021, Vol. 0 ›› Issue (12): 88-93.doi: 10.13474/j.cnki.11-2246.2021.379

基于LiDAR点云的建筑物分割深度学习模型研究

胡传文^1,2, 卢世杰¹, 杨文敬¹, 朱小勇¹

1. 浙江省测绘科学技术研究院, 浙江杭州 311100;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2021-09-02 修回日期:2021-10-20 发布日期:2021-12-30
通讯作者: 杨文敬。E-mail:yangwj126@163.com
作者简介:胡传文(1978-),男,博士,高级工程师,主要从事遥感和地理信息系统研究与应用工作。E-mail:chuanwenhu@qq.com

Deep learning architecture for building extraction using LiDAR point clouds

HU Chuanwen^1,2, LU Shijie¹, YANG Wenjing¹, ZHU Xiaoyong¹

1. Zhejiang Academy of Surveying and Mapping, Hangzhou 311100, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2021-09-02 Revised:2021-10-20 Published:2021-12-30

摘要/Abstract

摘要： 本文针对深度神经网络算法应用于机载激光点云进行大规模建筑物提取的问题,分别选取PointNet++和PointCNN两个网络模型进行了改进和对比。对于PointCNN,通过参数调整,使其更适合大场景信息提取。对于PointNet++,为了增加更多特征,加快大场景下网络模型的训练效率,在网络体系结构中添加了一种新的特征提取层——K-means层。另外,通过在测试数据集上的训练和验证发现,本文基于深度学习方法的分类较好地克服了点云的无序特性,能够更好地利用点之间的空间相关性,改进后两种模型的精度均达96%以上,在建筑物提取的时间效率和效果上优于原始模型。

关键词: PointNet++, PointCNN, 激光雷达, 点云, 建筑, K均值

Abstract: Aiming at the problem of applying deep neural network algorithm to LiDAR point cloud for large-scale building extraction, PointCNN and PointNet++ models are selected for modification and comparison in this paper. For PointCNN, the parameters are adjusted to make it more suitable for large scenes. For PointNet++, in order to add more features and speed up the training efficiency of network model in large scenes, a K-means layer is added after the sampling layer. Finally, through training and verification on the test data set, it is found that the deep learning methods can well solve the disordered characteristics of point cloud and make better use of the spatial correlation between points. The accuracy of the improved models is more than 96% and they are also better than the original models in time consumption and extraction effect.

Key words: PointNet++, PointCNN, LiDAR, point clouds, building, K-means

中图分类号:

P237

胡传文, 卢世杰, 杨文敬, 朱小勇. 基于LiDAR点云的建筑物分割深度学习模型研究[J]. 测绘通报, 2021, 0(12): 88-93.

HU Chuanwen, LU Shijie, YANG Wenjing, ZHU Xiaoyong. Deep learning architecture for building extraction using LiDAR point clouds[J]. Bulletin of Surveying and Mapping, 2021, 0(12): 88-93.

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基于LiDAR点云的建筑物分割深度学习模型研究

Deep learning architecture for building extraction using LiDAR point clouds

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