Real-time echosounder based acoustic seabed segmentation with two first echo parameters

Hamilton, L. J.

doi:10.1016/j.mio.2015.01.004

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…With appropriate ground-truth calibration for a particular environment or substrate type, and a means to statistically classify (supervised or unsupervised) derived echo parameters, this family of methods has met with some success (e.g. Amiri-Simkooei et al, 2011;Brown et al, 2011;Serpetti et al, 2011;Hamilton, 2014).…”

Section: Characterizing Benthic Substrates Through Sidescan Texture Amentioning

confidence: 99%

“…The energy within the second peak is calculated in an identical manner to Equation 26 using pixel locations E 2 , resulting in the e 2 coefficient or 'hardness' or 'impedance' parameter. In reality, e 1 and e 2 are simply acoustic parameters whose relationship to bed roughness and hardness, respectively, is not universal (Hamilton, 2014), requiring site-specific calibration (e.g. Hamilton et al, 1999;Serpetti et al, 2011).…”

Section: Characterizing Benthic Substrates Through Sidescan Texture Amentioning

confidence: 99%

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Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

Buscombe

2017

Environmental Modelling & Software

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In recent years, lightweight, inexpensive, vessel-mounted 'recreational grade' sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing 'democratization' of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.

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Section: Characterizing Benthic Substrates Through Sidescan Texture Amentioning

confidence: 99%

Section: Characterizing Benthic Substrates Through Sidescan Texture Amentioning

confidence: 99%

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

Buscombe

2017

Environmental Modelling & Software

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Large-scale variation in lakebed properties interpreted from single-beam sonar in two Laurentian Great Lakes

Pecoraro,

Esselman,

O'Brien

et al. 2023

Journal of Great Lakes Research

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Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

Cutter

Stierhoff

Demer

2016

ICES Journal of Marine Science

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The seabed can be classified using data from vertical, split-beam echosounders. This was demonstrated recently using a model parameterized with acoustic estimates of slope, roughness, normal-incidence backscattering strength, and variation of backscattering strength by frequency and incidence angle. These seabed classifications were interpreted and validated using published surficial geology maps, but the acoustic data indicated greater spatial variability. Here, classifications of sediment grain or feature size are ascribed to areas ∼10 m2. First, images of the seabed in the study area are ascribed, based on per cent coverage, to seven primary classes ranging from mud through high-relief rock, and 25 primary–secondary classes. Then, a refined seabed classifier, based on the acoustic model parameters is trained, using a nearest-neighbours algorithm, on a subset of the class data. The classifier accurately predicts 96% of the primary classes, and 93% of the primary–secondary classes from an independent data subset. These methods should be useful for characterizing, mapping, and quantifying potential seabed habitat domains of demersal fish and benthic invertebrates.

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Real-time echosounder based acoustic seabed segmentation with two first echo parameters

Cited by 4 publications

References 11 publications

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

Large-scale variation in lakebed properties interpreted from single-beam sonar in two Laurentian Great Lakes

Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

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