Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images
The freshwater resource is invaluable and indispensable for any nation like the Republic of Korea. Recently, deep learning (DL), AI models have become more popular and applied frequently for surface water studies. The Segment Anything Model (SAM) has been developing sharply and takes an adaptable ap...
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2025-01-01
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| author | Nguyen Hong Quang Hanna Lee Eui-Myoung Kim Gihong Kim |
| author_facet | Nguyen Hong Quang Hanna Lee Eui-Myoung Kim Gihong Kim |
| author_sort | Nguyen Hong Quang |
| collection | DOAJ |
| description | The freshwater resource is invaluable and indispensable for any nation like the Republic of Korea. Recently, deep learning (DL), AI models have become more popular and applied frequently for surface water studies. The Segment Anything Model (SAM) has been developing sharply and takes an adaptable approach with the ability to perform zero-shot inference. Although, pre-trained SAM was trained with millions of images (a billion masks), applying it to remote sensing data reveals limitations of inaccurate results and unlabeled classes, particularly for the more complex and noise data from Synthetic-aperture Radar Images. Hence, we fine-tune the SAM model and other popular CNN models of YOLOv8, U-net(ResNet50), and DeepLab(ResNet50, EfficientNet) for lake semantic segmentation using multi-SAR RS datasets of Kompsat-5, ALOS-2, Sentinel-1, and a combination of the three datasets (data link) for model result comparisons. This study’s accuracy assessment showed the SAM was the most precise model (accuracy overall <inline-formula> <tex-math notation="LaTeX">$\approx ~0.95$ </tex-math></inline-formula>) followed by the DeepLab(ResNet50), YOLOv8, U-net(ResNet50), and DeepLab(EfficientNet) model. Almost all models segmented highly accurate lake areas fitted well with ground-truth masks, nevertheless, the SAM (code link) presented the most well-performance model. The YOLO deals well with larger datasets and requires deeper trains to gain higher accuracy outputs. In addition, this research investigated the responses of each DL model to the SAR dataset proving a need for fine-tuning model results on SAR RS for better lake segmentations. |
| format | Article |
| id | doaj-art-869f7e9a40a84dfc8186d74bddee636d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-869f7e9a40a84dfc8186d74bddee636d2025-08-20T03:01:27ZengIEEEIEEE Access2169-35362025-01-01131727175010.1109/ACCESS.2024.351651910795130Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar ImagesNguyen Hong Quang0https://orcid.org/0000-0001-7657-624XHanna Lee1https://orcid.org/0000-0001-7862-8306Eui-Myoung Kim2https://orcid.org/0000-0002-2835-0526Gihong Kim3https://orcid.org/0000-0003-3280-5340Institute for Smart Infrastructure, Gangneung-Wonju National University, Gangneung-si, Gangwon-do, South KoreaInstitute for Smart Infrastructure, Gangneung-Wonju National University, Gangneung-si, Gangwon-do, South KoreaDepartment of Drone and GIS Engineering, Namseoul University, Cheonan, South KoreaDepartment of Civil and Environmental Engineering, Gangneung-Wonju National University, Gangneung-si, Gangwon-do, South KoreaThe freshwater resource is invaluable and indispensable for any nation like the Republic of Korea. Recently, deep learning (DL), AI models have become more popular and applied frequently for surface water studies. The Segment Anything Model (SAM) has been developing sharply and takes an adaptable approach with the ability to perform zero-shot inference. Although, pre-trained SAM was trained with millions of images (a billion masks), applying it to remote sensing data reveals limitations of inaccurate results and unlabeled classes, particularly for the more complex and noise data from Synthetic-aperture Radar Images. Hence, we fine-tune the SAM model and other popular CNN models of YOLOv8, U-net(ResNet50), and DeepLab(ResNet50, EfficientNet) for lake semantic segmentation using multi-SAR RS datasets of Kompsat-5, ALOS-2, Sentinel-1, and a combination of the three datasets (data link) for model result comparisons. This study’s accuracy assessment showed the SAM was the most precise model (accuracy overall <inline-formula> <tex-math notation="LaTeX">$\approx ~0.95$ </tex-math></inline-formula>) followed by the DeepLab(ResNet50), YOLOv8, U-net(ResNet50), and DeepLab(EfficientNet) model. Almost all models segmented highly accurate lake areas fitted well with ground-truth masks, nevertheless, the SAM (code link) presented the most well-performance model. The YOLO deals well with larger datasets and requires deeper trains to gain higher accuracy outputs. In addition, this research investigated the responses of each DL model to the SAR dataset proving a need for fine-tuning model results on SAR RS for better lake segmentations.https://ieeexplore.ieee.org/document/10795130/Fine-tunningRepublic of KoreareservoirSAMspace-borne SAR |
| spellingShingle | Nguyen Hong Quang Hanna Lee Eui-Myoung Kim Gihong Kim Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images IEEE Access Fine-tunning Republic of Korea reservoir SAM space-borne SAR |
| title | Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images |
| title_full | Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images |
| title_fullStr | Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images |
| title_full_unstemmed | Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images |
| title_short | Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images |
| title_sort | fine tunned segment anything model sam for reservoir extractions compared with popular cnns an experiment for space borne synthetic aperture radar images |
| topic | Fine-tunning Republic of Korea reservoir SAM space-borne SAR |
| url | https://ieeexplore.ieee.org/document/10795130/ |
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