Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images
Deep learning (DL) has become the mainstream technique for extracting information from high-spatial-resolution (HSR) imagery because of its powerful feature representation capabilities. However, DL models rely heavily on accurate annotations, which limits their generalizability to new data. Recently...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2491108 |
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| _version_ | 1849224372220329984 |
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| author | Mengyuan Yang Rui Yang Min Wang Haiyan Xu Gang Xu |
| author_facet | Mengyuan Yang Rui Yang Min Wang Haiyan Xu Gang Xu |
| author_sort | Mengyuan Yang |
| collection | DOAJ |
| description | Deep learning (DL) has become the mainstream technique for extracting information from high-spatial-resolution (HSR) imagery because of its powerful feature representation capabilities. However, DL models rely heavily on accurate annotations, which limits their generalizability to new data. Recently, the Segment Anything Model (SAM) has significantly advanced image segmentation techniques, showing great potential for use in remote sensing applications. To address the above limitations and explore the potential of the SAM for use with HSR imagery, we propose a novel method for completing semantic segmentation tasks that combines the SAM and unsupervised domain adaptation (UDA) techniques, enhancing model performance on unlabeled HSR imagery. Specifically, we propose a pseudolabel refinement module by integrating SAM and UDA techniques. Furthermore, the obtained pseudolabels are used to train the proposed self-training and SAM-based network (STSAMNet) for performing semantic segmentation; this network embeds two types of adapter layers to adapt the capabilities of the SAM to HSR imagery. During the training process, an iterative training strategy and a noise-weighted loss are applied to further improve the accuracy of the model on unlabeled images. Compared with other UDA methods, our method achieves the best performance in terms of F1 and mean intersection over union (mIoU) values. |
| format | Article |
| id | doaj-art-73c3a499bef047a9ab1bd33ab0cac976 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-73c3a499bef047a9ab1bd33ab0cac9762025-08-25T11:24:42ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2491108Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing imagesMengyuan Yang0Rui Yang1Min Wang2Haiyan Xu3Gang Xu4Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, People’s Republic of ChinaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, People’s Republic of ChinaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, People’s Republic of ChinaZhejiang College of Security Technology, Wenzhou, People’s Republic of ChinaZhejiang College of Security Technology, Wenzhou, People’s Republic of ChinaDeep learning (DL) has become the mainstream technique for extracting information from high-spatial-resolution (HSR) imagery because of its powerful feature representation capabilities. However, DL models rely heavily on accurate annotations, which limits their generalizability to new data. Recently, the Segment Anything Model (SAM) has significantly advanced image segmentation techniques, showing great potential for use in remote sensing applications. To address the above limitations and explore the potential of the SAM for use with HSR imagery, we propose a novel method for completing semantic segmentation tasks that combines the SAM and unsupervised domain adaptation (UDA) techniques, enhancing model performance on unlabeled HSR imagery. Specifically, we propose a pseudolabel refinement module by integrating SAM and UDA techniques. Furthermore, the obtained pseudolabels are used to train the proposed self-training and SAM-based network (STSAMNet) for performing semantic segmentation; this network embeds two types of adapter layers to adapt the capabilities of the SAM to HSR imagery. During the training process, an iterative training strategy and a noise-weighted loss are applied to further improve the accuracy of the model on unlabeled images. Compared with other UDA methods, our method achieves the best performance in terms of F1 and mean intersection over union (mIoU) values.https://www.tandfonline.com/doi/10.1080/17538947.2025.2491108Weakly supervised learningSAMbuilding extractiondomain adaptation |
| spellingShingle | Mengyuan Yang Rui Yang Min Wang Haiyan Xu Gang Xu Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images International Journal of Digital Earth Weakly supervised learning SAM building extraction domain adaptation |
| title | Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images |
| title_full | Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images |
| title_fullStr | Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images |
| title_full_unstemmed | Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images |
| title_short | Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images |
| title_sort | integrating unsupervised domain adaptation and sam technologies for image semantic segmentation a case study on building extraction from high resolution remote sensing images |
| topic | Weakly supervised learning SAM building extraction domain adaptation |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2491108 |
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