GEOSCAN, résultats de la recherche


TitreClassifying the seafloor using a textural segmentation approach
TéléchargerTéléchargement (publication entière)
LicenceVeuillez noter que la Licence du gouvernement ouvert - Canada remplace toutes les licences antérieures.
AuteurKoop, L; Snellen, M; Simons, D
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; par Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Commission géologique du Canada, Dossier public 8295, 2017 p. 67, (Accès ouvert)
LiensGeoHab 2017
ÉditeurRessources naturelles Canada
Réunion2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; mai 1-4, 2017
Documentdossier public
Mediaen ligne; numérique
Référence reliéeCette publication est contenue dans Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Commission géologique du Canada, Dossier public 8295
Sujetstechniques de cartographie; océanographie; milieux marins; études côtières; conservation; organismes marins; écologie marine; gestion des ressources; peuplements biologiques; etudes de l'environnement; écosystèmes; levés géophysiques; levés acoustiques marins; levés au sonar; sonar latéral; bathymétrie; topographie du fond océanique; analyses de textures; classifications des textures; échantillons prélevés au hasard; biologie; géologie marine; géologie des dépôts meubles/géomorphologie; géologie de l'environnement; géophysique
ProgrammeGéoscience en mer, Géoscience de la gestion des océans
Diffusé2017 09 26
Résumé(disponible en anglais seulement)
Seabed habitat mapping using multi-beam echo-sounder data is a very active field of research with direct uses in protecting ecologically important areas, marine resource management, and to set legislation to safeguard the oceans.
For seafloor classification, it is important to use the best data possible but it is also important to extract the most information from the available data. Seafloor classification is often done by directly using backscatter, bathymetry, and bathymetric derivative data produced by multi-beam echo-sounder systems. A way to extract more information from the above-mentioned data is to also use texture information from the bathymetry and/or backscatter.
In this study, texture based classification was performed on bathymetry data from the Borkumse Stenen and Bruine Bank within the Dutch sector of the North Sea. The method makes use of object-based image analysis (OBIA; using eCognition). The classification results are verified by using grab samples from the DINOloket database.
The performance of texture based classification will be examined when bathymetry data alone is used as input. It will be further investigated if including texture based in conjunction with backscatter, and bathymetry based classification improves classification performance of currently existing methods. It will also be examined if rule sets developed for one area of the sea can be used to classify the seafloor in another area and the effect that differing spatial resolutions of different datasets have on the portability of texture-based classification rule sets.