FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms
This study proposes the Feature-Enhanced Residual Attention U-Net (FERA-Net) model, which is trained using cloud mask label data generated by the semiautomatic CM7 method. The aim is to enhance the automation of water-ice cloud segmentation tasks and improve the segmentation accuracy based on existi...
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| Format: | Article |
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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/10820951/ |
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| author | Xu Ma Jialong Lai Zhicheng Zhong Feifei Cui |
| author_facet | Xu Ma Jialong Lai Zhicheng Zhong Feifei Cui |
| author_sort | Xu Ma |
| collection | DOAJ |
| description | This study proposes the Feature-Enhanced Residual Attention U-Net (FERA-Net) model, which is trained using cloud mask label data generated by the semiautomatic CM7 method. The aim is to enhance the automation of water-ice cloud segmentation tasks and improve the segmentation accuracy based on existing deep learning methods. Water ice clouds are a crucial component of Martian weather phenomena, and their precise description is important for understanding Martian climate changes. However, the Martian surface topography's complexity and the atmospheric environment's dynamic changes make accurate identification of water ice clouds difficult. To tackle this, we combine feature channels, spatial-channel attention bridge (SC), attention gating (AG), and a double convolution residual block (R2CBL) to enhance feature extraction and gradient stability. We introduce a new feature channel by calculating the ratio of the blue (B) and red (R) channels(B/R) to enhance the expression of cloud features. The SC, incorporating spatial attention blocks (SAB) and channel attention blocks (CAB), along with the AG, improves the model's ability to capture key features. The R2CBL, through double convolution operations and residual connections, addresses the gradient vanishing problem and enhances feature extraction. Experimental results demonstrate that, compared to existing deep learning methods, FERA-Net achieves significant improvements in the task of Martian water-ice cloud segmentation. Specifically, the F1-score increases from 75.16% to 89.73% (an improvement of 19.4%), and the Jaccard index improves from 78.93% to 90.01% (an increase of 14.1%).This research provides theoretical support for in-depth exploration of the Martian atmospheric environment and water cycle, and it can assist in planning and executing future Mars exploration missions. |
| format | Article |
| id | doaj-art-b23b35e9a8724f4fba31232401924f5b |
| 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-b23b35e9a8724f4fba31232401924f5b2025-01-25T00:00:09ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183797381110.1109/JSTARS.2025.352546610820951FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention MechanismsXu Ma0Jialong Lai1https://orcid.org/0000-0002-2157-2767Zhicheng Zhong2Feifei Cui3https://orcid.org/0009-0003-0304-3241School of Science, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Science, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Science, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Science, Jiangxi University of Science and Technology, Ganzhou, ChinaThis study proposes the Feature-Enhanced Residual Attention U-Net (FERA-Net) model, which is trained using cloud mask label data generated by the semiautomatic CM7 method. The aim is to enhance the automation of water-ice cloud segmentation tasks and improve the segmentation accuracy based on existing deep learning methods. Water ice clouds are a crucial component of Martian weather phenomena, and their precise description is important for understanding Martian climate changes. However, the Martian surface topography's complexity and the atmospheric environment's dynamic changes make accurate identification of water ice clouds difficult. To tackle this, we combine feature channels, spatial-channel attention bridge (SC), attention gating (AG), and a double convolution residual block (R2CBL) to enhance feature extraction and gradient stability. We introduce a new feature channel by calculating the ratio of the blue (B) and red (R) channels(B/R) to enhance the expression of cloud features. The SC, incorporating spatial attention blocks (SAB) and channel attention blocks (CAB), along with the AG, improves the model's ability to capture key features. The R2CBL, through double convolution operations and residual connections, addresses the gradient vanishing problem and enhances feature extraction. Experimental results demonstrate that, compared to existing deep learning methods, FERA-Net achieves significant improvements in the task of Martian water-ice cloud segmentation. Specifically, the F1-score increases from 75.16% to 89.73% (an improvement of 19.4%), and the Jaccard index improves from 78.93% to 90.01% (an increase of 14.1%).This research provides theoretical support for in-depth exploration of the Martian atmospheric environment and water cycle, and it can assist in planning and executing future Mars exploration missions.https://ieeexplore.ieee.org/document/10820951/Attention mechanismFERA-NetMartianresidual structurewater-ice clouds |
| spellingShingle | Xu Ma Jialong Lai Zhicheng Zhong Feifei Cui FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism FERA-Net Martian residual structure water-ice clouds |
| title | FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms |
| title_full | FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms |
| title_fullStr | FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms |
| title_full_unstemmed | FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms |
| title_short | FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms |
| title_sort | fera net a novel algorithm for mars water ice cloud segmentation integrating feature enhancement residual and attention mechanisms |
| topic | Attention mechanism FERA-Net Martian residual structure water-ice clouds |
| url | https://ieeexplore.ieee.org/document/10820951/ |
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