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With the increasing demand for detecting small water bodies in remote sensing imagery, particularly in Synthetic Aperture Radar (SAR) images, traditional water body extraction models often struggle to capture fine details and accurately delineate boundaries. To address these challenges, this paper introduces the integration of Frequency-Selective Deformable Convolution (FSDC) into the TransUNet architecture, optimizing water body extraction in SAR imagery. FSDC enhances feature representation in both the frequency and spatial domains. It does so through two key components: (1) The Frequency Selection Module, which employs Fourier transform to selectively enhance or suppress features across different frequency bands, thereby emphasizing the unique structure and boundaries of water bodies. (2) The Deformable Convolution Unit, which dynamically adjusts the receptive field via content-based sampling, allowing it to adapt to local variations at multiple scales and improve fine detail capture. After incorporating FSDC into the decoder of TransUNet, experimental results on the NY and C2S-MS datasets show a significant improvement in extraction accuracy, especially in detecting small water bodies. These findings underscore the effectiveness of the FSDC mechanism for small water body extraction from SAR imagery, offering a novel solution for precise water body analysis in remote sensing.