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Remote Sensing
Volume 17
Issue 3
10.3390/rs17030376
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Article

FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality

by
Bo Zhong
1,2,
Hongfeng Dan
1,2,
MingHao Liu
1,*,
Xiaobo Luo
1,
Kai Ao
2,
Aixia Yang
2 and
Junjun Wu
2,3
1
College of Computer Science and Technology, University of Posts and Telecommunications, Chongqing 400065, China
2
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
3
Hainan Aerospace Information Research Institute, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(3), 376; https://doi.org/10.3390/rs17030376
Submission received: 9 December 2024 / Revised: 13 January 2025 / Accepted: 20 January 2025 / Published: 23 January 2025
(This article belongs to the Section Environmental Remote Sensing)
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Abstract

The identification of roads from satellite imagery plays an important role in urban design, geographic referencing, vehicle navigation, geospatial data integration, and intelligent transportation systems. The use of deep learning methods has demonstrated significant advantages in the extraction of roads from remote sensing data. However, many previous deep learning-based road extraction studies overlook the connectivity and completeness of roads. To address this issue, this paper proposes a new high-resolution satellite road extraction network called FERDNet. In this paper, to effectively distinguish between road features and background features, we design a Multi-angle Feature Enhancement module based on the characteristics of remote sensing road data. Additionally, to enhance the extraction capability for narrow roads, we develop a High–Low-Level Feature Enhancement module within the directional feature extraction branch. Furthermore, experimental results on three public datasets validate the effectiveness of FERDNet in the task of road extraction from satellite imagery.
Keywords: road extraction; high-resolution satellite images; CNN; strip convolution road extraction; high-resolution satellite images; CNN; strip convolution

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MDPI and ACS Style

Zhong, B.; Dan, H.; Liu, M.; Luo, X.; Ao, K.; Yang, A.; Wu, J. FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality. Remote Sens. 2025, 17, 376. https://doi.org/10.3390/rs17030376

AMA Style

Zhong B, Dan H, Liu M, Luo X, Ao K, Yang A, Wu J. FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality. Remote Sensing. 2025; 17(3):376. https://doi.org/10.3390/rs17030376

Chicago/Turabian Style

Zhong, Bo, Hongfeng Dan, MingHao Liu, Xiaobo Luo, Kai Ao, Aixia Yang, and Junjun Wu. 2025. "FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality" Remote Sensing 17, no. 3: 376. https://doi.org/10.3390/rs17030376

APA Style

Zhong, B., Dan, H., Liu, M., Luo, X., Ao, K., Yang, A., & Wu, J. (2025). FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality. Remote Sensing, 17(3), 376. https://doi.org/10.3390/rs17030376

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