GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images
Semantic change detection (SCD) in remote sensing images can simultaneously obtain changed areas and the transformation of ground objects, providing finer grained information support in ground observation applications. Recently, multitask networks have become the main paradigm for SCD, but their det...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11017612/ |
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| author | Qing Ding Fengyan Wang Mingchang Wang Ying Zhang Gui Cheng |
| author_facet | Qing Ding Fengyan Wang Mingchang Wang Ying Zhang Gui Cheng |
| author_sort | Qing Ding |
| collection | DOAJ |
| description | Semantic change detection (SCD) in remote sensing images can simultaneously obtain changed areas and the transformation of ground objects, providing finer grained information support in ground observation applications. Recently, multitask networks have become the main paradigm for SCD, but their detection performance is still affected by limited multi-scale feature extraction and insufficient cross-task information interaction. To address these issues, we propose a global–local awareness integrated network (GLAI-Net) for SCD in remote sensing images. We design a parallel encoding structure and utilize convolutional neural networks and transformer to achieve multi-scale modeling of images and enhance feature expression ability. Meanwhile, we propose multi-scale feature fusion (MSFF) modules in GLAI-Net to enhance the focus of detail features on changed objects with different sizes. Between the classification and change detection decoding branches, we propose semantic change response (SCR) modules in GLAI-Net that fully utilize the correlation between different tasks to improve the consistency and accuracy of detection results. Qualitative and quantitative results on the Landsat-SCD and SECOND datasets indicate that the proposed GLAI-Net outperforms the comparison methods in SCD performance, with SeKs of 0.5182 and 0.2063, respectively. In addition, the ablation experiment results confirm the effectiveness of MSFF and SCR modules in improving feature diversity and optimizing SCD performance. |
| format | Article |
| id | doaj-art-b519533aa8f94ca59d519d7af2e7f38c |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b519533aa8f94ca59d519d7af2e7f38c2025-08-20T03:20:01ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118142911430710.1109/JSTARS.2025.357475511017612GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing ImagesQing Ding0https://orcid.org/0000-0002-8731-9596Fengyan Wang1https://orcid.org/0000-0002-3460-9928Mingchang Wang2https://orcid.org/0000-0002-2806-858XYing Zhang3Gui Cheng4College of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, ChinaSemantic change detection (SCD) in remote sensing images can simultaneously obtain changed areas and the transformation of ground objects, providing finer grained information support in ground observation applications. Recently, multitask networks have become the main paradigm for SCD, but their detection performance is still affected by limited multi-scale feature extraction and insufficient cross-task information interaction. To address these issues, we propose a global–local awareness integrated network (GLAI-Net) for SCD in remote sensing images. We design a parallel encoding structure and utilize convolutional neural networks and transformer to achieve multi-scale modeling of images and enhance feature expression ability. Meanwhile, we propose multi-scale feature fusion (MSFF) modules in GLAI-Net to enhance the focus of detail features on changed objects with different sizes. Between the classification and change detection decoding branches, we propose semantic change response (SCR) modules in GLAI-Net that fully utilize the correlation between different tasks to improve the consistency and accuracy of detection results. Qualitative and quantitative results on the Landsat-SCD and SECOND datasets indicate that the proposed GLAI-Net outperforms the comparison methods in SCD performance, with SeKs of 0.5182 and 0.2063, respectively. In addition, the ablation experiment results confirm the effectiveness of MSFF and SCR modules in improving feature diversity and optimizing SCD performance.https://ieeexplore.ieee.org/document/11017612/Deep learningglobal–local awarenessmultitask learningremote sensing imagessemantic change detection (SCD) |
| spellingShingle | Qing Ding Fengyan Wang Mingchang Wang Ying Zhang Gui Cheng GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning global–local awareness multitask learning remote sensing images semantic change detection (SCD) |
| title | GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images |
| title_full | GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images |
| title_fullStr | GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images |
| title_full_unstemmed | GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images |
| title_short | GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images |
| title_sort | glai net global x2013 local awareness integrated network for semantic change detection in remote sensing images |
| topic | Deep learning global–local awareness multitask learning remote sensing images semantic change detection (SCD) |
| url | https://ieeexplore.ieee.org/document/11017612/ |
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