Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | Marine geodesy |
| Frühes Online-Datum | 3 Okt. 2024 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 3 Okt. 2024 |
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.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Ozeanographie
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in: Marine geodesy, 03.10.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Robust algorithm for automatic surface-based outlier detection in MBES point clouds
AU - Mohammadivojdan, Bahareh
AU - Lorenz, Felix
AU - Artz, Thomas
AU - Weiβ, Robert
AU - Hake, Frederic
AU - Alkhatib, Yazan
AU - Neumann, Ingo
AU - Alkhatib, Hamza
N1 - Publisher Copyright: © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024/10/3
Y1 - 2024/10/3
N2 - 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.
AB - 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.
KW - MBES
KW - Outliers
KW - robust estimator
KW - surface model
UR - http://www.scopus.com/inward/record.url?scp=85205541694&partnerID=8YFLogxK
U2 - 10.1080/01490419.2024.2408684
DO - 10.1080/01490419.2024.2408684
M3 - Article
AN - SCOPUS:85205541694
JO - Marine geodesy
JF - Marine geodesy
SN - 0149-0419
ER -