TGB-UNet: Dual-Scale Topographic Gating and Topography-Guided Boundary Learning for Instantaneous Waterline Extraction on Tidal Flats

Extracting instantaneous waterlines on tidal flats is challenging because spectral confusion among turbid water, wet bare tidal flats, and residual channel water can cause misclassification and boundary displacement. This study proposes a Topography-Guided Boundary U-Net (TGB-UNet) for Hangzhou Bay using Gaofen-1 and Gaofen-6 multispectral imagery. The model incorporates slope and local relative elevation through dual-scale topographic gating and topography-guided auxiliary boundary learning. Compared with FCN-8s, vanilla U-Net, and HED-UNet, TGB-UNet achieved the best mean performance across six evaluation metrics. Its F1 and boundary F1 scores reached 98.44% and 83.52%, respectively, while the average symmetric surface distance (ASSD) was 50.13 m, a reduction of 44.41% relative to vanilla U-Net. Ablation experiments showed that direct terrain concatenation did not yield consistent gains, whereas gated fusion and topography-guided boundary learning improved waterline localization. Tidal-level-stratified comparisons showed the largest absolute reductions in ASSD and the 90th-percentile boundary distance under low-tidal-level conditions. A spatial holdout test in an adjacent coastal sector further showed that TGB-UNet retained its advantage over vanilla U-Net across all six metrics. These results indicate that incorporating static topographic priors through dual-scale gating and topography-guided auxiliary boundary learning helps improve instantaneous waterline localization at complex water–land interfaces on tidal flats.