RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism
With the rapid development of remote sensing (RS) technology, it has become more and more convenient to obtain multi-temporal RS images, which provides new opportunities for the research and development of change detection (CD) technology. However, existing methods still have shortcomings in recogni...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014070/ |
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| author | Chuanlu Li Xiaorong Xue Caijia Zeng Yifan Xu Xingbiao Xu Siyue Zhao |
| author_facet | Chuanlu Li Xiaorong Xue Caijia Zeng Yifan Xu Xingbiao Xu Siyue Zhao |
| author_sort | Chuanlu Li |
| collection | DOAJ |
| description | With the rapid development of remote sensing (RS) technology, it has become more and more convenient to obtain multi-temporal RS images, which provides new opportunities for the research and development of change detection (CD) technology. However, existing methods still have shortcomings in recognizing complex targets and accurately distinguishing the edges of change regions when performing CD in complex backgrounds. Therefore, we propose a novel CD model called RegCDNet, which is specifically designed to address the needs of RS image CD. The model employs RegNet as the backbone network for feature extraction, using a simple and efficient strategy to fuse shallow features. We designed an ac-dramit feature enhancement module (AFEM) that combines atrous convolution and a transformer containing a dual attention mechanism, which can efficiently capture long-range dependencies between pixels while focusing on local information. A dual-gated fusion module (DGFM) is also designed, which employs a dual gating mechanism for feature fusion and can dynamically adjust the fusion weights. The experiment achieved 91.22%, 92.84% and 82.52% F1 metrics on the LEVIR-CD, WHU-CD and SYSU-CD datasets, respectively. |
| format | Article |
| id | doaj-art-6d4b764c24234869b2ce2644dbdce4df |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6d4b764c24234869b2ce2644dbdce4df2025-08-20T03:06:04ZengIEEEIEEE Access2169-35362025-01-0113914669147910.1109/ACCESS.2025.357292811014070RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating MechanismChuanlu Li0https://orcid.org/0009-0001-5203-4275Xiaorong Xue1https://orcid.org/0000-0002-6443-4794Caijia Zeng2https://orcid.org/0009-0006-2435-8869Yifan Xu3https://orcid.org/0009-0002-1652-7555Xingbiao Xu4https://orcid.org/0009-0004-8408-0710Siyue Zhao5School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, ChinaWith the rapid development of remote sensing (RS) technology, it has become more and more convenient to obtain multi-temporal RS images, which provides new opportunities for the research and development of change detection (CD) technology. However, existing methods still have shortcomings in recognizing complex targets and accurately distinguishing the edges of change regions when performing CD in complex backgrounds. Therefore, we propose a novel CD model called RegCDNet, which is specifically designed to address the needs of RS image CD. The model employs RegNet as the backbone network for feature extraction, using a simple and efficient strategy to fuse shallow features. We designed an ac-dramit feature enhancement module (AFEM) that combines atrous convolution and a transformer containing a dual attention mechanism, which can efficiently capture long-range dependencies between pixels while focusing on local information. A dual-gated fusion module (DGFM) is also designed, which employs a dual gating mechanism for feature fusion and can dynamically adjust the fusion weights. The experiment achieved 91.22%, 92.84% and 82.52% F1 metrics on the LEVIR-CD, WHU-CD and SYSU-CD datasets, respectively.https://ieeexplore.ieee.org/document/11014070/Change detectionmulti-temporal remote sensing imagesatrous convolutionattention mechanismdual gating mechanism |
| spellingShingle | Chuanlu Li Xiaorong Xue Caijia Zeng Yifan Xu Xingbiao Xu Siyue Zhao RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism IEEE Access Change detection multi-temporal remote sensing images atrous convolution attention mechanism dual gating mechanism |
| title | RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism |
| title_full | RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism |
| title_fullStr | RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism |
| title_full_unstemmed | RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism |
| title_short | RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism |
| title_sort | regcdnet a regnet based framework for remote sensing image change detection combining feature enhancement and gating mechanism |
| topic | Change detection multi-temporal remote sensing images atrous convolution attention mechanism dual gating mechanism |
| url | https://ieeexplore.ieee.org/document/11014070/ |
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