FSDC: A Frequency-Selective Deformable Convolution for Water Body Extraction From Remote Sensing Images

With the growing demand for high-precision water body extraction in complex environments, traditional segmentation models continue to face challenges such as insufficient boundary detection and limited multiscale feature representation. To address the limitations of fixed dilation rates in conventional convolutional networks, which constrain the adaptability of receptive fields across varying spatial scales, this article proposes a novel segmentation module: the frequency-selective deformable convolution (FSDC). The FSDC consists of two key components: a frequency-selection module and a deformable convolution unit. The frequency-selection module performs frequency-domain decomposition via Fourier transform and applies learnable masks to selectively enhance or suppress different frequency bands, enabling the network to focus on the frequency-domain features of key spatial structures. The deformable convolution component introduces content-aware sampling through learned offsets, dynamically adjusting the receptive field in the spatial domain based on local geometric variations. This collaborative mechanism of the frequency-spatial dual domain not only strengthens the feature representation of multiscale spatial structures through frequency-domain filtering but also improves the capture of geometric details via adaptive sampling in the spatial domain, ultimately enhancing the model’s capability for multiscale and structurally adaptive feature modeling. We integrate FSDC into three mainstream architectures, namely UNet, Attention-UNet, and TransUNet, to construct enhanced models. Experimental results on high-resolution (HR), multisource remote sensing imagery demonstrate that the FSDC-introduced models significantly improve water body extraction performance, especially in terms of boundary accuracy, omission suppression, and false positive reduction. These findings underscore the potential of frequency-aware, adaptive convolutional mechanisms for advancing remote sensing applications such as water resource monitoring, ecological management, and land cover analysis.