Alluvial substrate mapping by automated texture segmentation of recreational-grade side scan sonar imagery

Daniel Hamill, Daniel Buscombe and Joseph M Wheaton

PLOS ONE, 2018, vol. 13, issue 3, 1-28

Abstract: Side scan sonar in low-cost ‘fishfinder’ systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits. We develop a method for automated segmentation of bed textures into between two to five grain-size classes. Second-order texture statistics are used in conjunction with a Gaussian Mixture Model to classify the heterogeneous bed into small homogeneous patches of sand, gravel, and boulders with an average accuracy of 80%, 49%, and 61%, respectively. Reach-averaged proportions of these sediment types were within 3% compared to similar maps derived from multibeam sonar.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0194373

DOI: 10.1371/journal.pone.0194373

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