DTFL: A Deep Learning Method for Digital Bathymetry Model Void-Filling Guided by Topographic Feature Line

Data voids significantly compromise the reliability of Digital Bathymetry Models (DBMs) for critical applications such as oceanographic research and resource exploration. Traditional interpolation methods struggle to accurately reconstruct complex submarine landforms, often resulting in blurred or distorted restorations. While deep learning methods offer promising solutions, the remarkable complexity and diversity of submarine landforms still pose significant challenges for DBM void-filling. To address this, we propose a novel two-stage deep learning framework leveraging topographic feature lines (TFLs), named DTFL. In the first stage, DTFL focuses on predicting TFL within void regions. In the second stage, it employs the predicted TFL as geometric constraints to guide structurally rational and high-fidelity restoration. Additionally, DTFL employs the proposed Enhanced-Transformer U-Net (ETU) architecture, which integrates the strengths of U-Net and Transformers to capture long-range dependencies among multiscale features. Crucially, a hierarchical integration strategy with a Feature Adaptive Alignment (FAA) module progressively injects the TFL information into the void-filling network at the feature level. This ensures that fine-grained spatial constraints with TFL features guide the reconstruction toward structurally rational reconstructions. Experiments demonstrate that our method outperforms existing approaches on both strip-shaped and random voids.