激光与光电子学进展, 2020, 57 (12): 122802, 网络出版: 2020-06-03   

融合光谱信息的机载LiDAR点云三维深度学习分类方法 下载: 1387次

3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information
作者单位
河南理工大学测绘与国土信息工程学院, 河南 焦作 454000
摘要
针对传统二维深度学习方法无法实现三维点云分类问题,提出一种融合光谱信息的机载LiDAR点云三维深度学习分类方法。该方法首先融合机载LiDAR点云和多光谱航空影像进行点云光谱信息扩充,进而在点云格网化和数据增强处理的基础上,采用多层感知机提取出不同尺度下的点特征和全局特征,最后基于三维深度学习算法实现机载LiDAR点云的准确分类。利用国际摄影测量与遥感协会提供的数据集进行所提方法验证,结果表明,融合光谱信息后,机载LiDAR点云分类精度提高了13.39个百分点;与已有方法的对比结果也进一步表明,所提方法可以在减少特征向量提取的情况下,取得较好的分类结果。
Abstract
Aim

ing at the problem that the traditional 2D deep learning method can not realize the 3D point cloud classification, this study proposes a novel classification method for airborne LiDAR point clouds based on 3D deep learning. First, airborne LiDAR point clouds and multi-spectral imagery are fused to expand the spectral information of point clouds. Then, 3D point clouds are placed on grids to make the LiDAR data suitable for the 3D deep learning. Subsequently, the local and global features in different scales are extracted by multi-layer perceptron. Finally, airborne LiDAR point clouds are classified into semantic objects using the 3D deep learning algorithm. The data sets provided by the International Society of Photogrammetry and Remote Sensing (ISPRS) are used to validate the proposed method, and the experimental results show that the classification accuracy can be increased by 13.39% by fusing the LiDAR point clouds and multi-spectral images. Compared with some of the methods submitted to ISPRS, the proposed method achieves better performance by simplifying the process of feature extraction.

王宏涛, 雷相达, 赵宗泽. 融合光谱信息的机载LiDAR点云三维深度学习分类方法[J]. 激光与光电子学进展, 2020, 57(12): 122802. Hongtao Wang, Xiangda Lei, Zongze Zhao. 3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122802.

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