Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Chen, Yuehong1; Shi, Kaixin1; Ge, Yong2; Zhou, Ya'nan1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:12 |
| 关键词 | Spatial resolution Image resolution Feature extraction Convolutional neural networks Remote sensing Spatiotemporal phenomena Sensors Multiscale feature spatiotemporal image fusion two-stream convolutional neural networks |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2021.3069116 |
| 通讯作者 | Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | Spatiotemporal remote sensing image fusion (STF) is a promising way to obtain remote sensing data with both fine spatial and temporal resolutions. Gradual and abrupt changes in land surface reflectance images are the main challenges in existing STF methods. Advanced deep learning techniques present powerful ability in learning image-changed information. Therefore, this article proposes a novel spatiotemporal image fusion method using multiscale two-stream convolutional neural networks (STFMCNNs). Multiscale two-stream convolutional neural networks are proposed to capture different sizes of objects in feature learning from a coarse spatial resolution image and two pairs of coarse and fine spatial resolution (FR) images at other dates. Meanwhile, temporal dependence and temporal consistency are explored as complementary information for STFMCNN. Moreover, a local fusion method is developed to characterize local variation by combining two predicted images derived from each stream. Two experiments on different real images are conducted to demonstrate the effectiveness of STFMCNN. Results show that STFMCNN outperformed three existing methods by predicting more accurate FR images with more preserved changed information. |
| WOS关键词 | MODIS ; REFLECTANCE ; LANDSAT |
| 资助项目 | National Key Research and Development Program of China[2017YFB0503501] ; National Natural Science Foundation of China[42071315] ; Fundamental Research Funds for the Central Universities[B210202008] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000730619400073 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169451]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China 2.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 | Chen, Yuehong,Shi, Kaixin,Ge, Yong,et al. Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:12. |
| APA | Chen, Yuehong,Shi, Kaixin,Ge, Yong,&Zhou, Ya'nan.(2022).Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,12. |
| MLA | Chen, Yuehong,et al."Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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