A feature perturbation weakly supervised learning network for airborne multispectral LiDAR pointcloud classification

A feature perturbation weakly supervised learning network for airborne multispectral LiDAR pointcloud classification

Currently, most pointcloud classification methods heavily rely on huge numbers of labeled samples. Notably, labeling a large-scale multispectral LiDAR (MS-LiDAR) pointcloud is time-consuming and costly. To address this issue, we propose a feature perturbation weakly supervised network for classifyin...

Full description

Bibliographic Details
Main Authors: Ke Chen, Haiyan Guan, Lanying Wang, Yongtao Yu, Yufu Zang, Nannan Qin, Jiacheng Liu, Jonathan Li
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Multispectral LiDAR
Weakly supervised learning
Pointcloud classification
Dual semantic inference structure
Consistency constraint
Mutual pseudo-labeling loss
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843224000372

Internet

http://www.sciencedirect.com/science/article/pii/S1569843224000372

Similar Items