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Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm

Marceau Michel 1 Julian Le Deunf 2, 3, 4 Nathalie Debese 5, 1 Laurène Bazinet 1 Loïc Dejoie 1 
3 Lab-STICC_DECIDE - Equipe DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
5 Lab-STICC_M3 - Equipe Marine Mapping & Metrology
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : The datasets acquired during hydrographic surveys contain outliers, i.e., soundings that do not describe the sea bottom. Many algorithms are developed to identify them. Here, we study unsupervised non-parametric algorithms with a densitybased approach. These algorithms make no assumption about the data and identify outliers as the data furthest away from their neighbors. We asses the ToMATo method developed by INRIA in 2009 to detect outlier soundings from multibeam echosounder data. This clustering algorithm combines a mode-seeking phase with a cluster merging phase using topological persistence. After the theoretical presentation of the ToMATo algorithm, we evaluate its performance on four data sets representing a wide variety of seabeds. We compare this method with the well-known DBSCAN and LOF algorithms. Finally, we suggest an application of the ToMATo algorithm to multibeam data acquired in extradetection mode, where topological persistence allows to form the most relevant clusters.
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https://hal-imt.archives-ouvertes.fr/hal-03583743
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Submitted on : Tuesday, February 22, 2022 - 9:13:37 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Monday, May 23, 2022 - 6:15:16 PM

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Marceau Michel, Julian Le Deunf, Nathalie Debese, Laurène Bazinet, Loïc Dejoie. Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm. OCEANS 2021: San Diego – Porto, Sep 2021, San Diego, United States. pp.1-8, ⟨10.23919/OCEANS44145.2021.9705930⟩. ⟨hal-03583743⟩

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