Robust algorithm for automatic surface-based outlier detection in MBES point clouds
- verfasst von
- Bahareh Mohammadivojdan, Felix Lorenz, Thomas Artz, Robert Weiβ, Frederic Hake, Yazan Alkhatib, Ingo Neumann, Hamza Alkhatib
- 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.
- Organisationseinheit(en)
-
Geodätisches Institut
- Externe Organisation(en)
-
Bundesanstalt für Gewässerkunde (BfG)
- Typ
- Artikel
- Journal
- Marine geodesy
- ISSN
- 0149-0419
- Publikationsdatum
- 03.10.2024
- Publikationsstatus
- Elektronisch veröffentlicht (E-Pub)
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Ozeanographie
- Elektronische Version(en)
-
https://doi.org/10.1080/01490419.2024.2408684 (Zugang:
Offen)
