Li et al. (2026) FSDC: A Frequency-Selective Deformable Convolution for Water Body Extraction From Remote Sensing Images

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zhenxuan Li, Miner Huang, X. R. Zhang, Zhiyong Lv, Tingye Tao, Min Yu, Yongchao Zhu, Shun Li, Xiaochuan Qu
• DOI: 10.1109/tgrs.2026.3651516

Research Groups

Not provided in the given text.

Short Summary

The paper introduces FSDC, a novel frequency-selective deformable convolution method, for enhanced water body extraction from remote sensing images.

Objective

• To develop and evaluate a Frequency-Selective Deformable Convolution (FSDC) method for accurate water body extraction from remote sensing imagery.

Study Configuration

• Spatial Scale: Implied to be regional to global, depending on remote sensing image resolution. Specific scale not provided in the given text.
• Temporal Scale: Not provided in the given text.

Methodology and Data

• Models used: FSDC (Frequency-Selective Deformable Convolution), likely a deep learning model based on convolutional neural networks.
• Data sources: Remote sensing images. Specific types (e.g., optical, SAR) not provided in the given text.

Main Results

Not provided in the given text.

Contributions

Not provided in the given text.

Funding

Not provided in the given text.

Citation

@article{Li2026FSDC,
  author = {Li, Zhenxuan and Huang, Miner and Zhang, X. R. and Lv, Zhiyong and Shi, Wenzhong and Tao, Tingye and Yu, Min and Zhu, Yongchao and Li, Shun and Qu, Xiaochuan},
  title = {FSDC: A Frequency-Selective Deformable Convolution for Water Body Extraction From Remote Sensing Images},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3651516},
  url = {https://doi.org/10.1109/tgrs.2026.3651516}
}