FDEN: Frequency-Band Decoupling Detail Enhancement Network for High-Fidelity Water Boundary Segmentation

Accurate extraction of water bodies in remote sensing images is crucial for natural disaster prediction, aquatic ecosystem monitoring, and resource management. However, most existing deep-learning-based methods primarily operate in the raw pixel space of images and fail to leverage the frequency characteristics of remote sensing images, resulting in an inability to fully exploit the representational power of deep models when predicting mask images. This paper proposes a Frequency-Band Decoupling Detail Enhancement Network (FDEN) to achieve high-precision water body extraction. The FDEN begins with an initial decoupling and enhancement stage for frequency information. Based on this multi-frequency representation, we further propose a Multi-Band Detail-Aware Module (MDAM), designed to adaptively enhance salient structural cues for water bodies across frequency bands while effectively suppressing irrelevant or noisy components. Extensive experiments demonstrate that the FDEN model outperforms state-of-the-art methods in terms of its segmentation accuracy and robustness.