Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network

被引:7
|
作者
Xiao, Kai [1 ]
Qian, Jia [2 ]
Li, Teng [3 ,4 ]
Peng, Yuanxi [1 ]
机构
[1] Natl Univ Def Technol, Coll Comp Sci & Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China
[2] Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200082, Peoples R China
[3] Natl Univ Def Technol, Beijing Inst Adv Study, Beijing 100020, Peoples R China
[4] Natl Univ Def Technol, Coll Adv Interdisciplinary Studies, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
multispectral LiDAR point cloud; deep learning network; semantic segmentation; FACE REPRESENTATION; 2-DIMENSIONAL PCA; CLASSIFICATION;
D O I
10.3390/rs15010243
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art.
引用
收藏
页数:19
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