High-quality super-resolution mapping using spatial deep learning
文献类型：期刊论文
| 作者 | Zhang, Xining8; Ge, Yong7,8; Chen, Jin5,6; Ling, Feng4; Wang, Qunming3; Du, Delin1; Xiang, Ru8 |
| 刊名 | ISCIENCE
 |
| 出版日期 | 2023-06-16 |
| 卷号 | 26期号:6页码:106875 |
| DOI | 10.1016/j.isci.2023.106875 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Super-resolution mapping (SRM) is a critical technology in remote sensing. Recently, several deep learning models have been developed for SRM. Most of these models, however, only use a single stream to process remote sensing images and mainly focus on capturing spectral features. This can undermine the quality of the resulting maps. To address this issue, we propose a soft information-constrained network (SCNet) for SRM that leverages spatial transition features represented by soft information as a spatial prior. Our network incorporates a separate branch to process prior spatial features for feature enhancement. SCNet can extract multi-level feature representations simultaneously from both remote sensing images and prior soft information and hierarchically incorporate features from soft information into image features. Experimental results on three datasets demonstrate that SCNet generates more complete spatial details in complex areas, providing an effective means for producing high-quality and high-resolution mapping products from remote sensing images. |
| WOS关键词 | PIXEL ; INFORMATION ; NETWORKS |
| WOS研究方向 | Science & Technology - Other Topics |
| WOS记录号 | WOS:001018669900001 |
| 出版者 | CELL PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194381]  |
| 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.Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China 4.Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing Pr, Beijing 100875, Peoples R China 5.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 6.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xining,Ge, Yong,Chen, Jin,et al. High-quality super-resolution mapping using spatial deep learning[J]. ISCIENCE,2023,26(6):106875. |
| APA | Zhang, Xining.,Ge, Yong.,Chen, Jin.,Ling, Feng.,Wang, Qunming.,...&Xiang, Ru.(2023).High-quality super-resolution mapping using spatial deep learning.ISCIENCE,26(6),106875. |
| MLA | Zhang, Xining,et al."High-quality super-resolution mapping using spatial deep learning".ISCIENCE 26.6(2023):106875. |
入库方式： OAI收割
来源：地理科学与资源研究所
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