Back to Search
Start Over
Alluvial substrate mapping by automated texture segmentation of recreational-grade side scan sonar imagery.
- Source :
- PLoS ONE; 3/14/2018, Vol. 13 Issue 3, p1-28, 28p
- Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- HABITATS
ALLUVIUM
SONAR imaging
TEXTURE mapping
IMAGE segmentation
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 128478847
- Full Text :
- https://doi.org/10.1371/journal.pone.0194373