A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery
文献类型：期刊论文
| 作者 | Yan, Xiangbing1; Song, Jia4; Liu, Yangxiaoyue; Lu, Shanlong2; Xu, Yuyue3; Ma, Chenyan1; Zhu, Yunqiang4 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2023-05-01 |
| 卷号 | 620页码:129561 |
| 关键词 | Lake extraction Deep learning Cloud shadow Transformer Semantic segmentation |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2023.129561 |
| 文献子类 | Article |
| 英文摘要 | Lake extraction from remote sensing images is an important approach to monitoring water resources. It provides guidance for aiding in maintaining natural ecological balance through the artificial adjustment of lake water resources. Cloud shadows are common interference factors in lake extraction from optical images. In this paper, we proposed a deep learning network based on a novel Transformer architecture to extract lakes from Sentinel-2 imagery. To determine how training datasets affect misclassifications of cloud shadows, we produced five training datasets that contained different proportions of cloud shadows to train the network; the highest pro-portion of cloud shadows among them was 4%. We found that, when the training dataset contained 4% cloud shadows, the obtained model could effectively reduce the misclassification of cloud shadows and improve the accuracy of lake extraction, and with this model, the evaluation results over the validation dataset achieved an overall accuracy (OA) of 0.9954 and a Kappa of 0.9568. In addition, the Transformer-based network was applied in the endorheic basin of Tibetan Plateau to further evaluate the generalization ability of the network over a large area. The Global Surface Water (GSW) dataset from the European Commission Joint Research Centre was used as a reference to validate our results in this area. Our experiment and results demonstrated the underlying potential of the Transformer-based network for improving image classification of optical imagery by reducing cloud shadow inferences. |
| 学科主题 | Engineering ; Geology ; Water Resources |
| WOS关键词 | MULTITEMPORAL LANDSAT DATA ; REMOTE-SENSING IMAGES ; TIBETAN PLATEAU ; NEURAL-NETWORKS ; SNOW DETECTION ; DEEP ; ALGORITHM ; LEVEL ; CLASSIFICATION ; REMOVAL |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193450]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100101, Peoples R China 4.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Xiangbing,Song, Jia,Liu, Yangxiaoyue,et al. A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery[J]. JOURNAL OF HYDROLOGY,2023,620:129561. |
| APA | Yan, Xiangbing.,Song, Jia.,Liu, Yangxiaoyue.,Lu, Shanlong.,Xu, Yuyue.,...&Zhu, Yunqiang.(2023).A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery.JOURNAL OF HYDROLOGY,620,129561. |
| MLA | Yan, Xiangbing,et al."A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery".JOURNAL OF HYDROLOGY 620(2023):129561. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。