EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network f…
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- DOI 10.1109/jstars.2025.3585311
- OpenAlex ID W4411949884
- Type Article
- Language EN
- Published 01 Jan 2025
- Volume 18
- Pages 17622–17637
- Source IEEE Journal of Selected Topics in Appli…
- Publisher Institute of Electrical and Electro…
- FWCI 2.07
- Countries 1
- Institutions 2
- Added 10 Oct 2025
- Updated 16 Oct 2026
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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land
Article 2025 EN Gold
Scene classification and mapping of surface mining-disturbed land can attain semantic-level information that is useful for monitoring mine geo-environment. Mining-land's complex characteristics make it difficult to extract key features, restricting the accuracy improvements. This study first constructed a 5-class dataset based on multispectral, synthetic aperture radar, and topographic images. Sub…
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6 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Xianju Li | China University of Geoscience… (CN) | |
| 2 | Pan Kong | China University of Geoscience… (CN) | — |
| 3 | Weitao Chen | China University of Geoscience… (CN) | |
| 4 | Wenxi He | China Geological Survey (CN) | |
| 5 | Jian Feng | China University of Geoscience… (CN) | |
| 6 | Jiangyuan Wang | China University of Geoscience… (CN) |
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