Extraction of Levees from Paddy Fields Based on the SE-CBAM UNet Model and Remote Sensing Images Article Swipe
Hongfu Ai
,
Xiaomeng Zhu
,
Yongqi Han
,
S. Ma
,
Yiang Wang
,
Yuanye Ma
,
Chuan Qin
,
Xinyi Han
,
Y Y Yang
,
Xinle Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17111871
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17111871
During rice cultivation, extracting levees helps to delineate effective planting areas, thereby enhancing the precision of management zones. This approach is crucial for devising more efficient water field management strategies and has significant implications for water-saving irrigation and fertilizer optimization in rice production. The uneven distribution and lack of standardization of levees pose significant challenges for their accurate extraction. However, recent advancements in remote sensing and deep learning technologies have provided viable solutions. In this study, Youyi Farm in Shuangyashan City, Heilongjiang Province, was chosen as the experimental site. We developed the SCA-UNet model by optimizing the UNet algorithm and enhancing its network architecture through the integration of the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation Networks (SE). The SCA-UNet model leverages the channel attention strengths of SE while incorporating CBAM to emphasize spatial information. Through a dual-attention collaborative mechanism, the model achieves a synergistic perception of the linear features and boundary information of levees, thereby significantly improving the accuracy of levee extraction. The experimental results demonstrate that the proposed SCA-UNet model and its additional modules offer substantial performance advantages. Our algorithm outperforms existing methods in both computational efficiency and precision. Significance analysis revealed that our method achieved overall accuracy (OA) and F1-score values of 88.4% and 90.6%, respectively. These results validate the efficacy of the multimodal dataset in addressing the issue of ambiguous levee boundaries. Additionally, ablation experiments using 10-fold cross-validation confirmed the effectiveness of the proposed SCA-UNet method. This approach provides a robust technical solution for levee extraction and has the potential to significantly advance precision agriculture.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17111871
- https://www.mdpi.com/2072-4292/17/11/1871/pdf?version=1748422861
- OA Status
- gold
- Cited By
- 4
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410828262
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410828262Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17111871Digital Object Identifier
- Title
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Extraction of Levees from Paddy Fields Based on the SE-CBAM UNet Model and Remote Sensing ImagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-05-28Full publication date if available
- Authors
-
Hongfu Ai, Xiaomeng Zhu, Yongqi Han, S. Ma, Yiang Wang, Yuanye Ma, Chuan Qin, Xinyi Han, Y Y Yang, Xinle ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs17111871Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/17/11/1871/pdf?version=1748422861Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/11/1871/pdf?version=1748422861Direct OA link when available
- Concepts
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Levee, Remote sensing, Extraction (chemistry), Environmental science, Geology, Geotechnical engineering, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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