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RMSENet: Multi-Scale Reverse Master–Slave Encoder Network for Remote Sensing Image Scene Classification

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成果类型:
期刊论文
作者:
Yongjun Wen;Jiake Zhou;Zhao Zhang;Lijun Tang*
通讯作者:
Lijun Tang
作者机构:
Hunan Province Higher Education Key Laboratory of Modeling and Monitoring on the Near-Earth Electromagnetic Environments, Changsha University of Science & Technology, Changsha 410114, China
School of Physics & Electronic Science, Changsha University of Science & Technology, Changsha 410114, China
Author to whom correspondence should be addressed.
[Yongjun Wen; Jiake Zhou; Zhao Zhang] Hunan Province Higher Education Key Laboratory of Modeling and Monitoring on the Near-Earth Electromagnetic Environments, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>School of Physics & Electronic Science, Changsha University of Science & Technology, Changsha 410114, China
[Lijun Tang] Hunan Province Higher Education Key Laboratory of Modeling and Monitoring on the Near-Earth Electromagnetic Environments, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>School of Physics & Electronic Science, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构:
[Lijun Tang] H
Hunan Province Higher Education Key Laboratory of Modeling and Monitoring on the Near-Earth Electromagnetic Environments, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>School of Physics & Electronic Science, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
remote sensing image;multi-scale reverse;master–slave encoder;wavelet
期刊:
Electronics
ISSN:
2079-9292
年:
2025
卷:
14
期:
12
页码:
2479-
基金类别:
This research received no external funding.
机构署名:
本校为第一且通讯机构
院系归属:
物理与电子科学学院
摘要:
Aiming at the problems that the semantic representation of information extracted by the shallow layer of the current remote sensing image scene classification network is insufficient, and that the utilization rate of primary visual features decreases with the deepening of the network layers, this paper designs a multi-scale reverse master–slave encoder network (RMSENet). It proposes a reverse cross-scale supplementation strategy for the slave encoder and a reverse cross-scale fusion strategy for the master encoder. This not only reversely supp...

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