TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection
In the field of remote sensing change detection, accurately capturing temporal change information and efficiently integrating multilevel information is a major challenge. In order to extend the sensory domain and optimize the information fusion, the model is able to capture temporal-spatial change f...
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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/10829977/ |
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| author | Xiaoyang Zhang Genji Yuan Zhen Hua Jinjiang Li |
| author_facet | Xiaoyang Zhang Genji Yuan Zhen Hua Jinjiang Li |
| author_sort | Xiaoyang Zhang |
| collection | DOAJ |
| description | In the field of remote sensing change detection, accurately capturing temporal change information and efficiently integrating multilevel information is a major challenge. In order to extend the sensory domain and optimize the information fusion, the model is able to capture temporal-spatial change features more accurately and improve the accuracy of change detection. In this article, we propose a temporal-spatial multiscale graph attention network (TSMGA), specifically, TSMGA employs a pair of pretrained ResNet18 for effective multiscale feature extraction, and in order to enhance the disparity information of the bitemporal images, we also design the temporal fusion block to emphasize the changed areas. The spatial and channel features of multiscale disparity features are enhanced by multiscale spatial-channel aggregation module. To enable more robust and efficient exploration of more global contextual information, we are inspired to introduce shortest path graph attention, which allows for a more informative and complete exploration of the global context, and furthermore allows for more efficient gathering information from far-off neighbors to the central node. In order to ensure comprehensive utilization of local and global features and to significantly improve the clarity and detail retention of the output image, global context residual fusion module (GCRFM) is designed, GCRFM efficiently utilizes the complementary information of the feature maps for fusing and recovering spatial details and variation information. We validate the effectiveness and advancement of TSMGA on three classical datasets (LEVIR-CD, WHU-CD, GZ-CD), and the experimental results show that TSMGA achieves the state-of-the-art performance level. |
| format | Article |
| id | doaj-art-51c13a1e9f4c4dee9d230e573fe721ad |
| institution | Kabale University |
| 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-51c13a1e9f4c4dee9d230e573fe721ad2025-01-25T00:00:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183696371210.1109/JSTARS.2025.352678510829977TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change DetectionXiaoyang Zhang0Genji Yuan1https://orcid.org/0000-0002-8710-2266Zhen Hua2https://orcid.org/0000-0003-1638-2974Jinjiang Li3https://orcid.org/0000-0002-2080-8678School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaIn the field of remote sensing change detection, accurately capturing temporal change information and efficiently integrating multilevel information is a major challenge. In order to extend the sensory domain and optimize the information fusion, the model is able to capture temporal-spatial change features more accurately and improve the accuracy of change detection. In this article, we propose a temporal-spatial multiscale graph attention network (TSMGA), specifically, TSMGA employs a pair of pretrained ResNet18 for effective multiscale feature extraction, and in order to enhance the disparity information of the bitemporal images, we also design the temporal fusion block to emphasize the changed areas. The spatial and channel features of multiscale disparity features are enhanced by multiscale spatial-channel aggregation module. To enable more robust and efficient exploration of more global contextual information, we are inspired to introduce shortest path graph attention, which allows for a more informative and complete exploration of the global context, and furthermore allows for more efficient gathering information from far-off neighbors to the central node. In order to ensure comprehensive utilization of local and global features and to significantly improve the clarity and detail retention of the output image, global context residual fusion module (GCRFM) is designed, GCRFM efficiently utilizes the complementary information of the feature maps for fusing and recovering spatial details and variation information. We validate the effectiveness and advancement of TSMGA on three classical datasets (LEVIR-CD, WHU-CD, GZ-CD), and the experimental results show that TSMGA achieves the state-of-the-art performance level.https://ieeexplore.ieee.org/document/10829977/Change detectionglobal contextremote sensingshortest path graph attention (SPGA)temporal fusion |
| spellingShingle | Xiaoyang Zhang Genji Yuan Zhen Hua Jinjiang Li TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection global context remote sensing shortest path graph attention (SPGA) temporal fusion |
| title | TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection |
| title_full | TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection |
| title_fullStr | TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection |
| title_full_unstemmed | TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection |
| title_short | TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection |
| title_sort | tsmga temporal spatial multiscale graph attention network for remote sensing change detection |
| topic | Change detection global context remote sensing shortest path graph attention (SPGA) temporal fusion |
| url | https://ieeexplore.ieee.org/document/10829977/ |
| work_keys_str_mv | AT xiaoyangzhang tsmgatemporalspatialmultiscalegraphattentionnetworkforremotesensingchangedetection AT genjiyuan tsmgatemporalspatialmultiscalegraphattentionnetworkforremotesensingchangedetection AT zhenhua tsmgatemporalspatialmultiscalegraphattentionnetworkforremotesensingchangedetection AT jinjiangli tsmgatemporalspatialmultiscalegraphattentionnetworkforremotesensingchangedetection |