Academic Journal
ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
| Title: | ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation |
|---|---|
| Authors: | Xinyang Pu, Hecheng Jia, Linghao Zheng, Feng Wang, Feng Xu |
| Source: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 4791-4804 (2025) |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| Subject Terms: | Adapter tuning, landcover classification, parameter-efficient fine-tuning, segment anything (SA), synthetic aperture radar (SAR), visual foundation model, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10849617/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2025.3532690 |
| Access URL: | https://doaj.org/article/eee3614af7ad45229ee58ec3621ceb51 |
| Accession Number: | edsdoj.3614af7ad45229ee58ec3621ceb51 |
| Database: | Directory of Open Access Journals |
| ISSN: | 19391404 21511535 |
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| DOI: | 10.1109/JSTARS.2025.3532690 |
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Language: | English |