Adaptive Super-Resolution for Ocean Bathymetric Maps Using a Deep Neural Network and Data Augmentation

Machine learning-based image super-resolution is a robust approach for obtaining detailed bathymetric maps. However, in machine learning using supervised data, dissimilarities in the features of training and target data sets degrades super-resolution performance. In this study, we propose a two-step method to generate training data with features similar to those of the target data using image transformation and composition. The super-resolution model trained using the proposed method on the Central Okinawa Trough data was applied to the bathymetry data around Okinotorishima Islands. The method improved the root mean squared error by up to 14.3% without compromising spatial consistency compared with that observed using conventional approaches, thus demonstrating the potential of combining artificial data generation with machine learning for super-resolution bathymetry mapping of the entire ocean floor. The proposed method, independent of the characteristics of training data, is suggested as a potential alternative to acoustic measurements for expanding areas of detailed bathymetric maps.