Abstract
Bathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
