A Deep Cross-Modal Fusion Network for Road Extraction With High-Resolution Imagery and LiDAR Data-Reference-Cited by-同舟云学术

A Deep Cross-Modal Fusion Network for Road Extraction With High-Resolution Imagery and LiDAR Data

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Luo Hui¹^ORCID,Wang Zijing¹^ORCID,Du Bo²^ORCID,Dong Yanni³^ORCID

Affiliation:

1. School of Computer Science, China University of Geosciences, Wuhan, China

2. School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and the Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China

3. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10439005.pdf?arnumber=10439005

Reference62 articles.

1. Road network detection from SPOT imagery for updating geographical information systems in the rural–urban fringe

2. State of the art on automatic road extraction for GIS update: a novel classification

3. GIS-based sustainable city compactness assessment using integration of MCDM, Bayes theorem and RADAR technology

4. A review of road extraction from remote sensing images

5. UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle

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