FTT: A Frequency-Aware Texture Matching Transformer for Digital Bathymetry Model Super-Resolution

Deep learning has shown significant advantages over traditional spatial interpolation methods in single image super-resolution (SISR). Recently, many studies have applied super-resolution (SR) methods to generate high-resolution (HR) digital bathymetry models (DBMs), but substantial differences between DBM and natural images have been ignored, which leads to serious distortions and inaccuracies. Given the critical role of HR DBM in marine resource exploitation, economic development, and scientific innovation, we propose a frequency-aware texture matching transformer (FTT) for DBM SR, incorporating global terrain feature extraction (GTFE), high-frequency feature extraction (HFFE), and a terrain matching block (TMB). GTFE has the capability to perceive spatial heterogeneity and spatial locations, allowing it to accurately capture large-scale terrain features. HFFE can explicitly extract high-frequency priors beneficial for DBM SR and implicitly refine the representation of high-frequency information in the global terrain feature. TMB improves fidelity of generated HR DBM by generating position offsets to restore warped textures in deep features. Experimental results have demonstrated that the proposed FTT has superior performance in terms of elevation, slope, aspect, and fidelity of generated HR DBM. Notably, the root mean square error (RMSE) of elevation in steep terrain has been reduced by 4.89 m, which is a significant improvement in the accuracy and precision of the reconstruction. This research holds significant implications for improving the accuracy of DBM SR methods and the usefulness of HR bathymetry products for future marine research.