Satellite-Driven Deep Learning Algorithm for Bathymetry Extraction

Accurate bathymetry using remotely sensed data is essential for various ocean-related fields such as marine resource exploration, environmental protection and offshore development. Traditional bathymetric techniques often face limitations in high-risk areas, whereas satellite-based methods offer advantages such as low cost and extensive coverage. This work aims to integrate the complementary strengths of ICESat-2 and Sentinel-2 satellites. We propose a novel dual-distance noise reduction algorithm to extract bathymetric information from ICESat-2 data, which is then integrated with Sentinel-2 optical imagery using a U-Net deep learning model. This approach enables precise inference of near-shore bathymetric distributions. Experimental results demonstrate the efficacy of the dual-distance noise reduction algorithm in accurately identifying photon signal points, achieving an average \(R^2\) of 0.906 and an RMSE of 0.778 m in bathymetric estimation. The study provides a robust scientific basis for active-passive fusion bathymetry inversion strategies in different scenarios.