TransRoadNet: A Novel Road Extraction Method for Remote Sensing Images via Combining High-Level Semantic Feature and Context-Reference-Cited by-同舟云学术

TransRoadNet: A Novel Road Extraction Method for Remote Sensing Images via Combining High-Level Semantic Feature and Context

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Yang Zhigang¹^ORCID,Zhou Daoxiang¹^ORCID,Yang Ying²,Zhang Jiapeng²,Chen Zehua¹

Affiliation:

1. College of Data Science, Taiyuan University of Technology, Taiyuan, China

2. Shanxi Transportation Technology Research and Development Company Ltd., Taiyuan, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanxi Province of China

Shanxi Transportation and Control Technology Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09766116.pdf?arnumber=9766116

Reference25 articles.

1. Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network;he;Int J Appl Earth Observ Geoinf,2022

2. A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

3. A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes

4. NL-LinkNet: Toward Lighter But More Accurate Road Extraction With Nonlocal Operations

5. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

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