Transfer Learning Based on Multi-Branch Architecture Feature Extractor for Airborne LiDAR Point Cloud Semantic Segmentation with Few Samples

Transfer Learning Based on Multi-Branch Architecture Feature Extractor for Airborne LiDAR Point Cloud Semantic Segmentation with Few Samples

The existing deep learning-based Airborne Laser Scanning (ALS) point cloud semantic segmentation methods require a large amount of labeled data for training, which is not always feasible in practice. Insufficient training data may lead to over-fitting. To address this issue, we propose a novel Multi...

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Bibliographic Details
Main Authors: Jialin Yuan, Hongchao Ma, Liang Zhang, Jiwei Deng, Wenjun Luo, Ke Liu, Zhan Cai
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
ALS
point cloud semantic segmentation
deep learning
transfer learning
few sample learning
Online Access:https://www.mdpi.com/2072-4292/17/15/2618
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