WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection
文献类型：期刊论文
| 作者 | Wang, Bin2; Jiang, Xiaohu2; Qin, Pinle; Zeng, Jianchao |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 17页码:14656-14669 |
| 关键词 | Feature extraction Transformers Data mining Correlation Semantics Context modeling Accuracy Change detection convolutional neural network (CNN) multiscale feature (MSF) fusion the whole process supervision transformer |
| DOI | 10.1109/JSTARS.2024.3439991 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Remote sensing image change detection aims to extract high-level semantic feature to identify the changed areas (CAs) between dual-temporal images (DTIs). However, the diversity in the CA shape and size poses certain challenge to the change detection (CD) task. Besides, different illumination conditions in the same scene of the DTI further increase the CD difficulty. In response to these above issues, this article proposes a multiscale feature fusion CD network-WSMsFNet, which fully utilizes the local and global information of multiscale features to achieve comprehensive representation of the change scene. In addition, the network improves the feature extraction ability of each module through the whole process supervision loss function. First, the network hierarchically extracts different scale information of the two temporal RS. Then, special information enhancement and fusion modules are constructed for various feature layers (i.e., the same level, adjacent level, and global features), aiming to enhance the local feature representation ability and contextual information relevance of the deep network. Finally, the whole-process loss function is set to supervise the intermediate layer learning, which can effectively enhance the feature representation ability and guide feature extraction direction of each module. Experiments have shown that the WSMsFNet has achieved significant results in both qualitative and quantitative indicators. |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001301018500019 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207991]  |
| 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.North Univ China, Dept Comp Sci & Technol, Taiyuan 030051, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Bin,Jiang, Xiaohu,Qin, Pinle,et al. WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:14656-14669. |
| APA | Wang, Bin,Jiang, Xiaohu,Qin, Pinle,&Zeng, Jianchao.(2024).WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,14656-14669. |
| MLA | Wang, Bin,et al."WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):14656-14669. |
入库方式： OAI收割
来源：地理科学与资源研究所
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