Yidi Wei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yidi Wei has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jiangchen Ke
Piesat Information Technology Co., Ltd., Beijing 100195, China
1 shared publication - Qing Xu
Faculty of Information Science and Engineering/Sanya Oceanographic Institution, Ocean University of China, Qingdao, China
1 shared publication - Xiaobin Yin
Faculty of Information Science and Engineering/Sanya Oceanographic Institution, Ocean University of China, Qingdao, China
1 shared publication - Xueding Li
Fujian Marine Forecasts, Fuzhou 350003, China
1 shared publication - Yongcun Cheng
Technology Innovation Center for South China Sea Remote Sensing, Surveying and Mapping Collaborative Application, Ministry of Natural Resources, Guangzhou, China
1 shared publication
Latest Publications
- Open Access
- 24 July 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (14) , 8526
Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.
- 24 July 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (14) , 8526
Open AccessDeep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.