GEOSCAN Search Results: Fastlink


TitleAn object-based seafloor classification tool using recognition of empirical angular backscatter signatures
DownloadDownload (whole publication)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorAlevizos, E
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B JORCID logo; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 33, Open Access logo Open Access
LinksGeoHab 2017
PublisherNatural Resources Canada
Meeting2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada
File formatpdf
Subjectsmarine geology; surficial geology/geomorphology; geophysics; mapping techniques; oceanography; bedrock geology; marine sediments; geophysical surveys; acoustic surveys, marine; sonar surveys; side-scan sonar; grab samples; photography; seafloor topography
ProgramOffshore Geoscience
Released2017 09 26
AbstractThis study presents a novel concept of seafloor acoustic mapping utilizing the angular dependence of high density soundings. A prerequisite is that data should result from a backscatter-dedicated survey (>100% swath overlap) in order to obtain small-scale seafloor areas ensonified from multiple incidence angles. Accordingly, backscatter data should be geometrically and radiometrically corrected in order to represent only variations due to seafloor type. This method is considered as a mixture of OBIA with empirical ARA and pattern recognition concepts and it provides supervised classification based on empirical backscatter angular signatures of a known set of seafloor types. Therefore it requires a library with all angular signatures corresponding to ground truth locations (seafloor type, dB and angle). The backscatter only needs to be stable and hence this approach is not only applicable on calibrated sonars but works for any MBES system that records backscatter in a stable way. The library should consist of sediment samples, underwater images and/or video which are used to drive the classification and validate its results. Ideally, the ground truth set should cover all different seafloor types from the study area. The concept is that angular backscatter signatures of known seafloor types that have been extracted from fine square areas of seafloor can be utilized for comparison with angular signatures of unknown seafloor. Initially, the study area is segmented into fine squares within which soundings from various beam-angles fall. The smaller the square size, the higher the seafloor homogeneity can be achieved; hence more representative angular backscatter signatures can be extracted for each seafloor type. In this study 5x5 m squares were used for representing naturally homogeneous seafloor. By extracting the angular signatures from the vicinity of sediment sample locations it was possible to use them as reference vectors for performing supervised classification. The classification works in the following way: vectors carrying the mean backscatter value per swath angle are being created from each group of soundings belonging to the same square. Following, each vector is compared to the reference vectors that represent ground-truthed seafloor types. The comparison tests whether the backscatter values of the vector under-comparison fall within a user-defined envelope (range of values) above and below the mean backscatter values of the reference vectors. If the backscatter values for the majority (>85%) of corresponding swath angles belong to the envelope of a reference vector, then these soundings are assigned with the class number of the reference vector. Empirical ARA is more flexible in describing seafloor heterogeneity, compared to physical backscatter models, therefore allowing for classification of a wider variety of seafloor types in a consistent way.
Summary(Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia, Canada.

Date modified: