Research on deep-sea terrain refinement methods based on satellite remote sensing data

Bathymetric data is crucial for navigation safety and various applications. However, only a small portion of the seafloor has been systematically mapped through direct measurement. The remaining bathymetry is estimated from satellite altimeter data, providing only an approximate representation of the seafloor's shape. To address this issue, the convolutional neural network (CNN) is introduced for the analysis of seabed topography. To the best of our knowledge, this marks the first attempt to incorporate the CNN into the definitive map of the world ocean floor. By employing skip connections based on residual learning, we construct a deeper CNN layer that effectively reduces training complexity. The deeper convolutional neural network layer is used to create a more complex nonlinear mapping model, which is then employed to downscale low-resolution seabed topographic data in order to obtain high-precision seabed topographic information. This process enhances the representation of seabed topographic data to reflect the actual terrain more accurately. The results are compared with those obtained using the bicubic difference method. Conducting tests near the Gulf of Thailand, where Malaysia Airlines Flight MH370 was lost, the results demonstrate that the method proposed in this paper achieves higher accuracy in processing the downscaling of seabed topographic data.