%0 Conference Proceedings
%T Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm
%+ École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)
%+ Service Hydrographique et Océanographique de la Marine (SHOM)
%+ Equipe DECIDE (Lab-STICC_DECIDE)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information  (IMT Atlantique - LUSSI)
%+ Equipe Marine Mapping & Metrology (Lab-STICC_M3)
%A Michel, Marceau
%A Le Deunf, Julian
%A Debese, Nathalie
%A Bazinet, Laurène
%A Dejoie, Loïc
%< avec comité de lecture
%( OCEANS 2021: San Diego – Porto
%B OCEANS 2021: San Diego – Porto
%C San Diego, United States
%I IEEE
%P 1-8
%8 2021-09-20
%D 2021
%R 10.23919/OCEANS44145.2021.9705930
%K Data processing
%K Multibeam data
%K Outlier detection
%K ToMATo clustering
%Z Sciences of the Universe [physics]/Earth Sciences/Oceanography
%Z Statistics [stat]/Machine Learning [stat.ML]
%Z Computer Science [cs]/Data Structures and Algorithms [cs.DS]
%Z Computer Science [cs]
%Z Environmental SciencesConference papers
%X 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.
%G English
%2 https://hal-imt.archives-ouvertes.fr/hal-03583743/document
%2 https://hal-imt.archives-ouvertes.fr/hal-03583743/file/2021149647.pdf
%L hal-03583743
%U https://hal-imt.archives-ouvertes.fr/hal-03583743
%~ SDE
%~ UNIV-BREST
%~ INSTITUT-TELECOM
%~ ENSTA-BRETAGNE
%~ CNRS
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%~ GIP-BE
%~ ENIB
%~ LAB-STICC
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA
%~ IMT-ATLANTIQUE
%~ INSTITUTS-TELECOM
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