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Multibeam echosounder data cleaning through a hierarchic adaptive and robust local surfacing

Nathalie Debese 1 Roderic Moitié 1 Nicolas Seube 1
1 OSM
STIC - Pôle STIC [Brest]
Abstract : Multibeam echo sounders (MBES) datasets generally contain sporadic outlier points. The huge volumes of MBES datasets in a hydrographic framework require the use of semi-automatic techniques. In very shallow waters depth, data cleaning becomes a challenging task when potential dangers to navigation have to be carefully checked. The aim of our paper is to attempt this goal by combining two well-known techniques. The seafloor is constructed as an assemblage of surface elements with the help of a robust statistical approach. The local parameters model is a priori chosen, its scale is driven through a quadtree descending approach using subdivision rules based on both statistical and spatio-temporal inferences. Our multi resolution approach provides, with the algorithm outputs, a classification map that notes areas of concern.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-00672672
Contributeur : Annick Billon-Coat <>
Soumis le : mardi 21 février 2012 - 17:05:40
Dernière modification le : mardi 11 février 2020 - 12:08:17

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Nathalie Debese, Roderic Moitié, Nicolas Seube. Multibeam echosounder data cleaning through a hierarchic adaptive and robust local surfacing. Computers & Geosciences, Elsevier, 2012, 46, pp.330-339. ⟨10.1016/j.cageo.2012.01.012⟩. ⟨hal-00672672⟩

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