High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout

High-resolution (HR) digital bathymetric models (DBMs) are essential for marine science studies. Currently, there are very few areas where detailed seafloor surveys have been conducted, which is insufficient to provide insight into the geological and oceanographic processes of our planet. In recent years, HR DBMs have been successfully reconstructed from low-resolution (LR) DBMs using deep learning. However, HR DBM reconstruction has high uncertainties due to the complexity of seafloor topography. Meanwhile, deep-learning-based methods do not provide uncertainty in the HR DBM reconstruction results. Here, based on a deep convolutional neural network with skip connection and network in network (DCSCN), we propose a modified model, DCSCN-DBM, to reconstruct HR DBMs from LR DBMs and evaluate reconstruction uncertainties using the dropout technique. DCSCN-DBM is a stacked convolutional layer in the original DCSCN replaced by several residual blocks, which effectively improves the performance of the network. However, the structure of network in network (NIN) in them is not favorable for reconstruction. Furthermore, dropout with appropriate probabilities at the right positions is also found to be not only beneficial or noninfluential for deep-learning-based HR DBM reconstruction but also to provide reliable reconstruction uncertainty. In regions with large errors, the DCSCN-DBM without NIN can provide more reliable uncertainty and accurate reconstruction. These results can offer HR DBMs with confidence level reconstructions for other areas where DBMs are needed to better address downstream tasks, such as habitat mapping, climate modeling, hazard prediction, resource assessment, and many other field applications.