ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
In the realm of artificial intelligence, the emergence of foundation models, backed by high computing capabilities and extensive data, has been revolutionary. A segment anything model (SAM), built on the vision transformer (ViT) model with millions of parameters and trained on its corresponding larg...
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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/10849617/ |
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| author | Xinyang Pu Hecheng Jia Linghao Zheng Feng Wang Feng Xu |
| author_facet | Xinyang Pu Hecheng Jia Linghao Zheng Feng Wang Feng Xu |
| author_sort | Xinyang Pu |
| collection | DOAJ |
| description | In the realm of artificial intelligence, the emergence of foundation models, backed by high computing capabilities and extensive data, has been revolutionary. A segment anything model (SAM), built on the vision transformer (ViT) model with millions of parameters and trained on its corresponding large-scale dataset SA-1B, excels in various segmentation scenarios relying on its significance of semantic information and generalization ability. Such achievement of visual foundation model stimulates continuous researches on specific downstream tasks in computer vision. The classwise-SAM-adapter (CWSAM) is designed to adapt the high-performing SAM for landcover classification on space-borne synthetic aperture radar (SAR) images. The proposed CWSAM freezes most of SAM's parameters and incorporates lightweight adapters for parameter-efficient fine-tuning, and a classwise mask decoder is designed to achieve semantic segmentation task. This adapt-tuning method allows for efficient landcover classification of SAR images, balancing the accuracy with computational demand. In addition, the task-specific input module injects low-frequency information of SAR images by MLP-based layers to improve the model performance. Compared to conventional state-of-the-art semantic segmentation algorithms by extensive experiments, CWSAM showcases enhanced performance with fewer computing resources, highlighting the potential of leveraging foundational models such as SAM for specific downstream tasks in the SAR domain. |
| format | Article |
| id | doaj-art-eee3614af7ad45229ee58ec3621ceb51 |
| 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-eee3614af7ad45229ee58ec3621ceb512025-02-11T00:00:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184791480410.1109/JSTARS.2025.353269010849617ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic SegmentationXinyang Pu0https://orcid.org/0009-0002-0627-4603Hecheng Jia1https://orcid.org/0000-0001-7538-4094Linghao Zheng2Feng Wang3https://orcid.org/0000-0002-2378-9126Feng Xu4https://orcid.org/0000-0002-7015-1467Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, ChinaKey Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, ChinaKey Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, ChinaKey Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, ChinaKey Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, ChinaIn the realm of artificial intelligence, the emergence of foundation models, backed by high computing capabilities and extensive data, has been revolutionary. A segment anything model (SAM), built on the vision transformer (ViT) model with millions of parameters and trained on its corresponding large-scale dataset SA-1B, excels in various segmentation scenarios relying on its significance of semantic information and generalization ability. Such achievement of visual foundation model stimulates continuous researches on specific downstream tasks in computer vision. The classwise-SAM-adapter (CWSAM) is designed to adapt the high-performing SAM for landcover classification on space-borne synthetic aperture radar (SAR) images. The proposed CWSAM freezes most of SAM's parameters and incorporates lightweight adapters for parameter-efficient fine-tuning, and a classwise mask decoder is designed to achieve semantic segmentation task. This adapt-tuning method allows for efficient landcover classification of SAR images, balancing the accuracy with computational demand. In addition, the task-specific input module injects low-frequency information of SAR images by MLP-based layers to improve the model performance. Compared to conventional state-of-the-art semantic segmentation algorithms by extensive experiments, CWSAM showcases enhanced performance with fewer computing resources, highlighting the potential of leveraging foundational models such as SAM for specific downstream tasks in the SAR domain.https://ieeexplore.ieee.org/document/10849617/Adapter tuninglandcover classificationparameter-efficient fine-tuningsegment anything (SA)synthetic aperture radar (SAR)visual foundation model |
| spellingShingle | Xinyang Pu Hecheng Jia Linghao Zheng Feng Wang Feng Xu ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adapter tuning landcover classification parameter-efficient fine-tuning segment anything (SA) synthetic aperture radar (SAR) visual foundation model |
| title | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
| title_full | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
| title_fullStr | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
| title_full_unstemmed | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
| title_short | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
| title_sort | classwise sam adapter parameter efficient fine tuning adapts segment anything to sar domain for semantic segmentation |
| topic | Adapter tuning landcover classification parameter-efficient fine-tuning segment anything (SA) synthetic aperture radar (SAR) visual foundation model |
| url | https://ieeexplore.ieee.org/document/10849617/ |
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