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TitreDevelopment of a machine learning technique for automatic analysis of seafloor image data: Case example Pogonophora coverage at mud volcanoes
AuteurLüdtke, A; Jerosch, K; Herzog, O; Schlüter, M
SourceComputers and Geosciences vol. 39, 2011 p. 120-128, https://doi.org/10.1016/j.cageo.2011.06.020 (Accès ouvert)
Année2011
Séries alt.Secteur des sciences de la Terre, Contribution externe 20100298
ÉditeurElsevier BV
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.1016/j.cageo.2011.06.020
Mediaen ligne; numérique
Formatspdf
Lat/Long OENS 14.7083 14.7333 72.0083 72.0000
Sujetstopographie du fond océanique; topographie du fond océanique; volcans de boue; techniques de cartographie; caractéristiques sous-marines; géologie marine; géophysique
Illustrationsimages; location maps
ProgrammeGestionnaire de programme, Géoscience en mer
Résumé(disponible en anglais seulement)
Digital image processing provides powerful tools for fast and precise analysis of large image data sets in marine and geoscientific applications. Because of the increasing volume of georeferenced image and video data acquired by underwater platforms such as remotely operated vehicles, means of automatic analysis of the acquired image data are required. A new and fast-developing application is the combination of video imagery and mosaicking techniques for seafloor habitat mapping. In this article we introduce an approach to fully automatic detection and quantification of Pogonophora coverage in seafloor video mosaics from mud volcanoes. The automatic recognition is based on textural image features extracted from the raw image data and classification using machine learning techniques. Classification rates of up to 98.86% were achieved on the training data. The approach was extensively validated on a data set of more than 4000 seafloor video mosaics from the Håkon Mosby Mud Volcano.
GEOSCAN ID287249