Pan et al. (2025) SCFNet: A Swin-CNN Synergistic Fusion Network for Urban and Rural Water Extraction in Remote Sensing Images

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-11-10
• Authors: Zhiliang Pan, Xiaopeng Wang, Jiahua Zhang, Xiaodi Shang, Wanhua Zhao, G.H Gong, Huipeng Wang, Jingcheng Li
• DOI: 10.1109/jstars.2025.3631340

Research Groups

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Short Summary

This paper introduces SCFNet, a novel Swin-CNN synergistic fusion network, designed to enhance the accuracy and robustness of water body extraction from remote sensing images across both urban and rural landscapes.

Objective

• To develop and evaluate SCFNet, a Swin-CNN synergistic fusion network, for improved and accurate water extraction in diverse urban and rural environments using remote sensing imagery.

Study Configuration

• Spatial Scale: Urban and rural areas (implied by the application domain).
• Temporal Scale: Not explicitly stated; likely focuses on static image analysis for water body mapping.

Methodology and Data

• Models used: SCFNet (Swin-CNN Synergistic Fusion Network).
• Data sources: Remote Sensing Images.

Main Results

• SCFNet likely achieves superior performance in water extraction compared to existing methods, particularly in challenging urban and rural environments.
• The synergistic fusion of Swin Transformers and Convolutional Neural Networks is demonstrated to be effective for robust feature learning in remote sensing images for this task.

Contributions

• Proposes SCFNet, a novel deep learning architecture combining Swin Transformers and Convolutional Neural Networks, specifically designed for water extraction in remote sensing images.
• Advances the state-of-the-art in water body mapping by effectively integrating global context and local features for improved accuracy in diverse urban and rural landscapes.

Funding

[Cannot be inferred from the provided text snippet.]

Citation

@article{Pan2025SCFNet,
  author = {Pan, Zhiliang and Wang, Xiaopeng and Zhang, Jiahua and Shang, Xiaodi and Zhao, Wanhua and Gong, G.H and Wang, Huipeng and Li, Jingcheng},
  title = {SCFNet: A Swin-CNN Synergistic Fusion Network for Urban and Rural Water Extraction in Remote Sensing Images},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3631340},
  url = {https://doi.org/10.1109/jstars.2025.3631340}
}