Z
Zhiwei Li
Researcher at Wuhan University
Publications - 377
Citations - 7642
Zhiwei Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Interferometric synthetic aperture radar & Computer science. The author has an hindex of 36, co-authored 269 publications receiving 4637 citations. Previous affiliations of Zhiwei Li include RWTH Aachen University & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan,Huanfeng Shen,Tongwen Li,Zhiwei Li,Shuwen Li,Yun Jiang,Hongzhang Xu,Weiwei Tan,Qianqian Yang,Jiwen Wang,Jianhao Gao,Liangpei Zhang +11 more
TL;DR: The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced.
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Microplastic pollution in the rivers of the Tibet Plateau
Changbo Jiang,Lingshi Yin,Zhiwei Li,Xiaofeng Wen,Xin Luo,Shuping Hu,Hanyuan Yang,Yuannan Long,Bin Deng,Lingzhi Huang,Yizhuang Liu +10 more
TL;DR: It is demonstrated that rivers in the Tibet Plateau have been contaminated by microplastics, not only in developed areas with intense human activity but also in remote areas, where microplastic pollution requires further attention.
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Resolving three-dimensional surface displacements from InSAR measurements: A review
TL;DR: In this paper, a systematic review of the progress achieved in this field is provided, based on an analysis of the InSAR Line-Of-Sight (LOS) measurements, i.e., Offset Tracking and multi-aperture InSar (MAI), with which the surface displacement in the azimuth direction can be measured together with the LOS displacement.
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Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
TL;DR: The experimental results show that MSCFF achieves a higher accuracy than the traditional rule-based cloud detection methods and the state-of-the-art deep learning models, especially in bright surface covered areas.
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Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
TL;DR: An automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery over land and results indicate that MFC performs well under different land conditions, and the average cloud classification accuracy of MFC is as high as 98.3%.