Robust algorithm for automatic surface-based outlier detection in MBES point clouds

authored by

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.

Organisation(s)

Geodetic Institute

External Organisation(s)

German Federal Institute of Hydrology (BfG)

Type

Article

Journal

Marine geodesy

ISSN

0149-0419

Publication date

03.10.2024

Publication status

E-pub ahead of print

Peer reviewed

Yes

ASJC Scopus subject areas

Oceanography

Electronic version(s)

https://doi.org/10.1080/01490419.2024.2408684 (Access: Open)

 

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