Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery
Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain c...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/676 |
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| author | Bojie Chen Qianran Zhang Na Yang Xiukun Wang Xiaobo Zhang Yilan Chen Shengli Wang |
| author_facet | Bojie Chen Qianran Zhang Na Yang Xiukun Wang Xiaobo Zhang Yilan Chen Shengli Wang |
| author_sort | Bojie Chen |
| collection | DOAJ |
| description | Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability. |
| format | Article |
| id | doaj-art-5fb1f6b720804d78a75063e3568fe698 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-5fb1f6b720804d78a75063e3568fe6982025-08-20T03:12:19ZengMDPI AGRemote Sensing2072-42922025-02-0117467610.3390/rs17040676Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 ImageryBojie Chen0Qianran Zhang1Na Yang2Xiukun Wang3Xiaobo Zhang4Yilan Chen5Shengli Wang6College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaFirst Institute of Oceanography, Ministry of Natural Resources (MNR), Qingdao 266061, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaTidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability.https://www.mdpi.com/2072-4292/17/4/676tidal creek segmentationResBlockU-Netattention mechanismremote sensingcoastal wetlands |
| spellingShingle | Bojie Chen Qianran Zhang Na Yang Xiukun Wang Xiaobo Zhang Yilan Chen Shengli Wang Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery Remote Sensing tidal creek segmentation ResBlock U-Net attention mechanism remote sensing coastal wetlands |
| title | Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery |
| title_full | Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery |
| title_fullStr | Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery |
| title_full_unstemmed | Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery |
| title_short | Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery |
| title_sort | deep learning extraction of tidal creeks in the yellow river delta using gf 2 imagery |
| topic | tidal creek segmentation ResBlock U-Net attention mechanism remote sensing coastal wetlands |
| url | https://www.mdpi.com/2072-4292/17/4/676 |
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