U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal
文献类型：期刊论文
| 作者 | Zhang, Qian1; Liu, Xiangnan1; Peng, Tao1; Yang, Xiao1; Tang, Mengzhen1; Zou, Xinyu2; Liu, Meiling1; Wu, Ling1; Zhang, Tingwei1 |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2024-12-31 |
| 卷号 | 61期号:1页码:19 |
| ISSN号 | 1548-1603 |
| 关键词 | Missing information reconstruction optical time series data SAR-to-optical translation spatiotemporal mapping deep learning |
| DOI | 10.1080/15481603.2024.2330185 |
| 通讯作者 | Liu, Xiangnan(liuxn@cugb.edu.cn) |
| 英文摘要 | Optical remotely sensed time series data have various key applications in Earth surface dynamics. However, cloud cover significantly hampers data analysis and interpretation. Despite synthetic aperture radar (SAR)-to-optical image translation techniques emerging as a promising solution, their effectiveness is diminished by their inability to adequately account for the intertwined nature of temporal and spatial dimensions. This study introduces U-SeqNet, an innovative model that integrates U-Net and Sequence-to-Sequence (Seq2Seq) architectures. Leveraging a pioneering spatiotemporal teacher forcing strategy, U-SeqNet excels in adapting and reconstructing data, capitalizing on available cloud-free observations to improve accuracy. Rigorous assessments through No Reference and Full Reference Image Quality Assessments (NR - IQA and FR - IQA) affirm U-SeqNet's exceptional performance, marked by a Natural Image Quality Evaluator (NIQE) score of 5.85 and Mean Absolute Error (MAE) of 0.039. These results underline U-SeqNet's exceptional capabilities in image reconstruction and its potential to improve remote sensing analysis by enabling more accurate and efficient multimodal and multitemporal cloud removal techniques. |
| WOS关键词 | REMOTE-SENSING IMAGE ; SAR ; ALGORITHM ; NETWORK ; FUSION ; SHADOW |
| 资助项目 | National Natural Science Foundation of China under Grant[41871223] ; National Natural Science Foundation of China |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001185057000001 |
| 资助机构 | National Natural Science Foundation of China under Grant ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203581]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Xiangnan |
| 作者单位 | 1.China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Qian,Liu, Xiangnan,Peng, Tao,et al. U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal[J]. GISCIENCE & REMOTE SENSING,2024,61(1):19. |
| APA | Zhang, Qian.,Liu, Xiangnan.,Peng, Tao.,Yang, Xiao.,Tang, Mengzhen.,...&Zhang, Tingwei.(2024).U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal.GISCIENCE & REMOTE SENSING,61(1),19. |
| MLA | Zhang, Qian,et al."U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal".GISCIENCE & REMOTE SENSING 61.1(2024):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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