标题: MCTN-Net: A Multiclass Transportation Network Extraction Method Combining Orientation and Semantic Features
作者: Shao, CL (Shao, Chenglin); Li, HF (Li, Huifang); Shen, HF (Shen, Huanfeng)
来源出版物: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 卷: 21 文献号: 6005205 DOI: 10.1109/LGRS.2024.3372194 Published Date: 2024
摘要: Transportation network extraction based on deep learning has become a hotspot. However, the existing models all aim to distinguish between background and transportation networks, while ignoring the class attributes within the transportation networks. In this letter, we propose a multiclass transportation network extraction network (MCTN-Net) to simultaneously extract railways, roadways, trails, and bridges. Inspired by multitask learning, the network first extracts the orientation and semantic information together by the use of a dense feature shared encoder (DFSE). The orientation and semantic features are then fused in the orientation-guided stacking module (OGSM) to enhance the connection between transportation network pixels. Furthermore, a semantic refinement branch (SRB) is designed to improve the ability to classify different transportation network types through deep supervised fusion and class attention. A multiclass transportation network dataset (MCTN dataset) was constructed and used in the experiments. The experiential results indicate that the proposed method achieves a mean intersection over union (MIoU) of 64.29% and a frequency-weighted intersection over union (FWIoU) of 71.20% without the background, which is significantly better than the other road extraction models and semantic segmentation methods. The code and dataset are available at https://github.com/fzzfRS/MCTN-Net.
作者关键词: Transportation; Semantics; Feature extraction; Bridges; Roads; Rail transportation; Data mining; Multiclass transportation network (MCTN) extraction; orientation learning; semantic feature refinement
地址: [Shao, Chenglin; Li, Huifang; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Li, Huifang; Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.
通讯作者地址: Li, HF (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
电子邮件地址: chenglinshao@whu.edu.cn; huifangli@whu.edu.cn; shenhf@whu.edu.cn
影响因子:4.8
版权所有 © 武汉大学资源与环境科学学院
地址:湖北省武汉市珞喻路129号 邮编:430079
电话:027-68778381,68778284,68778296 传真:027-68778893 邮箱:sres@whu.edu.cn