Learning from Synthetic Data: Enhancing Refraction Correction Accuracy for Airborne Image-Based Bathymetric Mapping of Shallow Coastal Waters - Agrafiotis, Panagiotis; Karantzalos, Konstantinos; Georgopoulos, Andreas; Skarlatos, Dimitrios (My University, 2021)
The increasing need for accurate bathymetric mapping is essential for a plethora of offshore activities. Even though aerial image datasets through Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques can provide a low-cost alternative compared to LiDAR and SONAR, offering additionally, important visual information, water refraction poses significant obstacles in delivering accurate bathymetry. In this article, the generation of manned and unmanned airborne synthetic datasets of dry and water covered areas is presented. These data are used to train models for correcting the geometric effects of refraction on real-world image-based point clouds and aerial images. Based on a thorough evaluation, important improvements are presented, indicating the increased accuracy and the reduced noise in the point clouds of the derived bathymetric products, meeting also the International Hydrographic Organization’s (IHO) standards.
Keywords: Airborne; Bathymetry; Coastal mapping; Machine learning; Refraction correction; Shallow waters; Support vector regression; Synthetic data; UAV