Alluvial Substrate Mapping by Automated Texture Segmentation of Recreational-Grade Side Scan Sonar Imagery

Authors

Daniel Hamill, Utah State UniversityFollow
Daniel Buscombe, Northern Arizona University
Joseph Michael Wheaton, Utah State UniversityFollow

Document Type

Article

Journal/Book Title/Conference

PLoS ONE

Volume

13

Issue

3

Publisher

Public Library of Science

Publication Date

3-14-2018

Keywords

alluvial, substrate mapping, automated texture, segmentation, recreational-grade, sonar, imagery

First Page

1

Last Page

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.

Recommended Citation

Hamill, Daniel; Buscombe, Daniel; and Wheaton, Joseph Michael, "Alluvial Substrate Mapping by Automated Texture Segmentation of Recreational-Grade Side Scan Sonar Imagery" (2018). Watershed Sciences Faculty Publications. Paper 1007.
https://digitalcommons.usu.edu/wats_facpub/1007