Automated, Objective Texture Segmentation of Multibeam
Echosounder Data

Seafloor Survey and Substrate Maps from James Island to Ozette Lake, Washington Outer Coast

Steven S. Intelmann1, George R. Cutter2, and Jonathan D. Beaudoi3
1Olympic Coast National Marine Sanctuary, NOAA
2Southwest Fisheries Science Center, NOAA
3Ocean Mapping Group, University of New Brunswick

Conservation
Automated, Objective Texture Segmentation of Multibeam
Echosounder Data (pdf, 10.8 MB)
Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example, management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology.

With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS).

Keywords: Benthic, habitat mapping, sediment classification, multibeam echosounder, local Fourier histogram texture features, essential fish habitat, Olympic Coast National Marine Sanctuary

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