Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling
Remote sensing ship detection is crucial for maritime surveillance, especially with the rise of synthetic aperture radar (SAR) images, known for their all-weather capabilities. Yet, annotating SAR images is resource-intensive and requires specialized expertise. Facing the challenge, we propose an un...
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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/10993287/ |
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| author | Yirong Yang Xi Yang Dong Yang |
| author_facet | Yirong Yang Xi Yang Dong Yang |
| author_sort | Yirong Yang |
| collection | DOAJ |
| description | Remote sensing ship detection is crucial for maritime surveillance, especially with the rise of synthetic aperture radar (SAR) images, known for their all-weather capabilities. Yet, annotating SAR images is resource-intensive and requires specialized expertise. Facing the challenge, we propose an unsupervised domain adaptation method for SAR ship detection based on multitask decoupling, which leverages data annotated with optical images instead of SAR annotations. In domain adaptation object detection, conflict arises between transferability required for adaptation and discriminability required for detection. Similarly, within object detection, conflict in feature preferences between classification and regression tasks occurs. We address these conflicts by decoupling the adaptation module from the detector module and decoupling the adaptation module into category, and bounding box adaptation modules. Our detector module integrates a feature enhancement network (FEN) to improve the classification and localization ability under the challenge of dense small targets with low visual contrast in SAR images. The adaptation module employs adversarial adaptation and cross-domain category alignment (CCA) to pull up the feature representations of different domains of ships for domain alignment. In addition, the cross-domain image synthesis (CDIS) module generates transitional images covering SAR domain style and optical domain content to bridge the huge gap between the domains. Through the separation and coordination of different modules, our multitask decoupling framework strategically balances feature transferability and discriminability. Extensive experiments on classic remote sensing datasets validate the effectiveness of our method, showing better performance than state-of-the-art methodologies. |
| format | Article |
| id | doaj-art-1f84254a53bd48faba0d1a3817948657 |
| 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-1f84254a53bd48faba0d1a38179486572025-08-20T03:12:36ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118126841269610.1109/JSTARS.2025.356821210993287Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask DecouplingYirong Yang0Xi Yang1https://orcid.org/0000-0002-5791-3674Dong Yang2State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, ChinaState Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, ChinaXi'an Institute of Space Radio Technology, Xi'an, ChinaRemote sensing ship detection is crucial for maritime surveillance, especially with the rise of synthetic aperture radar (SAR) images, known for their all-weather capabilities. Yet, annotating SAR images is resource-intensive and requires specialized expertise. Facing the challenge, we propose an unsupervised domain adaptation method for SAR ship detection based on multitask decoupling, which leverages data annotated with optical images instead of SAR annotations. In domain adaptation object detection, conflict arises between transferability required for adaptation and discriminability required for detection. Similarly, within object detection, conflict in feature preferences between classification and regression tasks occurs. We address these conflicts by decoupling the adaptation module from the detector module and decoupling the adaptation module into category, and bounding box adaptation modules. Our detector module integrates a feature enhancement network (FEN) to improve the classification and localization ability under the challenge of dense small targets with low visual contrast in SAR images. The adaptation module employs adversarial adaptation and cross-domain category alignment (CCA) to pull up the feature representations of different domains of ships for domain alignment. In addition, the cross-domain image synthesis (CDIS) module generates transitional images covering SAR domain style and optical domain content to bridge the huge gap between the domains. Through the separation and coordination of different modules, our multitask decoupling framework strategically balances feature transferability and discriminability. Extensive experiments on classic remote sensing datasets validate the effectiveness of our method, showing better performance than state-of-the-art methodologies.https://ieeexplore.ieee.org/document/10993287/Domain adaptationremote sensing imagessynthetic aperture radar (SAR) imagesship detection |
| spellingShingle | Yirong Yang Xi Yang Dong Yang Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Domain adaptation remote sensing images synthetic aperture radar (SAR) images ship detection |
| title | Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling |
| title_full | Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling |
| title_fullStr | Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling |
| title_full_unstemmed | Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling |
| title_short | Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling |
| title_sort | unsupervised domain adaptation for sar ship detection based on multitask decoupling |
| topic | Domain adaptation remote sensing images synthetic aperture radar (SAR) images ship detection |
| url | https://ieeexplore.ieee.org/document/10993287/ |
| work_keys_str_mv | AT yirongyang unsuperviseddomainadaptationforsarshipdetectionbasedonmultitaskdecoupling AT xiyang unsuperviseddomainadaptationforsarshipdetectionbasedonmultitaskdecoupling AT dongyang unsuperviseddomainadaptationforsarshipdetectionbasedonmultitaskdecoupling |