Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network
Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited pe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/439 |
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| author | Wu Feng Xiulin Geng Xiaoyu He Miao Hu Jie Luo Meihua Bi |
| author_facet | Wu Feng Xiulin Geng Xiaoyu He Miao Hu Jie Luo Meihua Bi |
| author_sort | Wu Feng |
| collection | DOAJ |
| description | Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications. |
| format | Article |
| id | doaj-art-7f5b3bd1ca604a4784cdea1e6e96705d |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-7f5b3bd1ca604a4784cdea1e6e96705d2025-08-20T03:43:22ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113343910.3390/jmse13030439Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution NetworkWu Feng0Xiulin Geng1Xiaoyu He2Miao Hu3Jie Luo4Meihua Bi5School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaAntarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications.https://www.mdpi.com/2077-1312/13/3/439sea ice extractiontrue-color imagerysentinel-2 imagedeep learningfeature enhancementgraph convolution network |
| spellingShingle | Wu Feng Xiulin Geng Xiaoyu He Miao Hu Jie Luo Meihua Bi Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network Journal of Marine Science and Engineering sea ice extraction true-color imagery sentinel-2 image deep learning feature enhancement graph convolution network |
| title | Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network |
| title_full | Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network |
| title_fullStr | Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network |
| title_full_unstemmed | Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network |
| title_short | Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network |
| title_sort | antarctic sea ice extraction for remote sensing images via modified u net based on feature enhancement driven by graph convolution network |
| topic | sea ice extraction true-color imagery sentinel-2 image deep learning feature enhancement graph convolution network |
| url | https://www.mdpi.com/2077-1312/13/3/439 |
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