Synergistic fusion network for landslide segmentation: the vital complementarity of geological information to remote sensing imagery-Reference-Cited by-同舟云学术

Synergistic fusion network for landslide segmentation: the vital complementarity of geological information to remote sensing imagery

Published:2024-05-17 Issue: Volume: Page:1-14
ISSN:1744-5760
Container-title:International Journal of Parallel, Emergent and Distributed Systems
language:en
Short-container-title:International Journal of Parallel, Emergent and Distributed Systems

Author:

Yao Guangle¹²³,Zhou Chuyi³,Zhou Haoran⁴⁵,Ye Shaoze⁴⁵,Yang Yi¹,Zhang Xiaodong³,Wang Honghui²³

Affiliation:

1. Chinese Academy of Surveying and Mapping, Beijing, People's Republic of China

2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, Sichuan, People's Republic of China

3. College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, Sichuan, People's Republic of China

4. Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University), Nanchang, Jiangxi, People's Republic of China

5. Shenzhen Sensing Data Technology Co., Ltd., Shenzhen, Guangdong, People's Republic of China

Funder

Sichuan Science and Technology Program

State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17445760.2024.2352881

Reference17 articles.

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3. Ronneberger O Fischer P Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference Munich Germany October 5–9 2015 Proceedings Part III 18. Springer; 2015. p. 234–241.

4. Chen L-C Zhu Y Papandreou G et al. Encoder–decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV); Munich Germany; 2018. p. 801–818.

5. Road Extraction by Deep Residual U-Net