A Seabed Terrain Feature Extraction Transformer for the Super-Resolution of the Digital Bathymetric Model

Abstract

The acquisition of high-resolution (HR) digital bathymetric models (DBMs) is crucial for oceanic research activities. However, obtaining HR DBM data is challenging, which has led to the use of super-resolution (SR) methods to improve the DBM’s resolution, as, unfortunately, existing interpolation methods for DBMs suffer from low precision, which limits their practicality. To address this issue, we propose a seabed terrain feature extraction transform model that combines the seabed terrain feature extraction module with the efficient transform module, focusing on the terrain characteristics of DBMs. By taking advantage of these two modules, we improved the efficient extraction of seabed terrain features both locally and globally, and as a result, we obtained a highly accurate SR reconstruction of DBM data within the study area, including the Mariana Trench in the Pacific Ocean and the adjacent sea. A comparative analysis with bicubic interpolation, SRCNN, SRGAN, and SRResNet shows that the proposed method decreases the root mean square error (RMSE) by 16%, 10%, 13%, and 12%, respectively. These experimental results confirm the high accuracy of the proposed method in terms of reconstructing HR DBMs.

Underwater Terrain Matching Method Based on Pulse-Coupled Neural Network for Unmanned Underwater Vehicles

“…The nodes that make up the terrestrial terrain are interdependent. The concentrated placement of these nodes on the terrestrial landscape not only provides elevation information but also conveys surface metadata such as entropy, slope, gradient, roughness, and other relevant parameters, thereby enhancing the informational richness of the surface terrain nodes [14][15][16]. The current terrain matching algorithm assumes that the discrepancy in error among all sounding data has the same impact on the terrain matching positioning, disregarding the variation in individual errors.…”