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Satellite Derived Active-Passive Fusion Bathymetry based on Gru Model

Aiming at the needs and difficulties of shallow bathymetry in sea areas lacking in-situ depth information, taking Dongdao Island of Xisha Islands as an example, the active-passive fusion bathymetry using ICESat-2 photon counting lidar and WorldView-2 optical remote sensing image based on GRU deep learning model is studied. First, tidal corrections are carried out separately for multi-track ICESat-2 data, and the depth data are obtained through signal point extraction and refraction correction. Then, taking the depth data as control points, the GRU deep learning model is applied to carry out water depth inversion in the study area. The experiments show that the MAE and MRE of the bathymetric inversion for the whole area are 1.1m and 17.3% respectively. Furthermore, the MAE and MRE are 1.3m and 19.3% in a local area without Lidar data. Active-passive fusion bathymetry based on GRU model can provide a new means for shallow water depth detection.