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

PLoS One. 2018 Mar 14;13(3):e0194373. doi: 10.1371/journal.pone.0194373. eCollection 2018.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Image Processing, Computer-Assisted*
  • Models, Theoretical*
  • Ultrasonic Waves*

Grant support

This work was funded by the Glen Canyon Dam Adaptive Management Program administered by the U.S. Bureau of Reclamation. The lead author (DH) and last author (JW) were supported by the U.S. Geological Survey to Utah State University (USGS Agreement G14AC00369; USU Award 150155).