Earth surface anomaly detection using spectral-semantic feature fusion and graph neural networks

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Earth surface anomaly detection using spectral-semantic feature fusion and graph neural networks
“Timely and accurate detection of surface anomalies is of great significance for ensuring the safety of human life and property. The research team explored the topic of multi category surface anomaly detection based on single satellite images, fused spectral and semantic features, and introduced graph neural network inference to establish a detection system that balances accuracy and efficiency, providing a feasible technical path for disaster emergency response.”
- role：First author第一作者
- Affiliation：
  School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
- Email：wei_ao@whu.edu.cn
- Introduction：敖伟，研究方向为计算机视觉、遥感地表异常检测。E-mail：wei_ao@whu.edu.cn
AO Wei
1 * ,
- role：Corresponding author通信作者
- Affiliation：
  School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
  Guangxi Communications Design Group Co., Ltd., Nanning 530029, China
- Email：15078888596@163.com
- Introduction：王长海，研究方向为机器视觉、无人系统、即时遥感。E-mail：15078888596@163.com
WANG Changhai
23 * ,
- Affiliation：
  School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
GAO Zhi
1 * ,
- Affiliation：
  School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
CHEN Boan
1 * ,
- Affiliation：
  School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
ZHAI Yuyang
1 * ,
- Affiliation：
  School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
GONG Xinyu
1 * ,
- Affiliation：
  Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
WANG Qiao
4 *
Pages: 1-15(2026)
DOI：10.11834/jrs.20266042
Accepted：

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