|Title||Classifying the seafloor using a textural segmentation approach|
|Download||Download (whole publication) |
|Author||Koop, L; Snellen, M; Simons, D|
|Source||Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 67,
https://doi.org/10.4095/305875 (Open Access)|
|Publisher||Natural Resources Canada|
|Meeting||2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017|
|Related||This publication is contained in Todd, B J; Brown, C J;
Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Geological Survey of Canada, Open File 8295|
|Subjects||marine geology; surficial geology/geomorphology; environmental geology; geophysics; mapping techniques; oceanography; marine environments; coastal studies; conservation; marine organisms; marine
ecology; resource management; biological communities; environmental studies; ecosystems; geophysical surveys; acoustic surveys, marine; sonar surveys; side-scan sonar; bathymetry; seafloor topography; textural analyses; textural classifications; grab
samples; geological mapping; geological mapping techniques; biology; habitat mapping; habitat conservation; habitat management; high latitude mapping; object-based image analysis|
|Program||Offshore Geoscience, Ocean Management Geoscience|
|Released||2017 09 26|
|Abstract||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.