A Slope-Assisted Back Propagation Method for Bathymetric Mapping

Machine learning (ML) technology has been successfully applied to Satellite Derived Bathymetry (SDB) using the water surface reflectance. Many studies have proved that it is not enough to derive water depth accurately using water surface reflectance only, and the integration of additional features into ML models will be helpful for water depth inversion. In this paper, a slope-assisted back propagation model (SBP) is proposed to obtain water bathymetry. The slope feature is used in this model by iterative optimization. Besides, data augmentation (DA) technology is also used to improve the training of BP models. We validated our model on datasets with different sample size from 4 research areas. Compared with the Log-ratio model, the mean absolute error (MAE) is reduced between 0.3m and 0.7m, the relative reduction rate of MAE can reach 67%, and the mean square error (MSE) is also relatively reduced by more than 38%. Compared with the conventional ML models, the relative reduction rate of MAE is 11%, and the MSE is relatively reduced by 24%. Experimental results show that the prediction accuracy of the proposed SBP model can be improved effectively; it provides an effective solution for bathymetric retrieval tasks.