Next Article in Journal
Transient Thermal Effects in Sedimentary Basins with Normal Faults and Magmatic Sill Intrusions—A Sensitivity Study
Next Article in Special Issue
Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods
Previous Article in Journal
A Joint Sedimentation-Flood Retention Assessment of a Small Water Reservoir in Slovakia: A New Hope for Old Reservoirs?
Previous Article in Special Issue
Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network

1
Leibniz Institute for Baltic Sea Research Warnemünde, 18119 Rostock, Germany
2
Coastal Geology and Sedimentology, Institute of Geosciences, University of Kiel, 24118 Kiel, Germany
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(4), 159; https://doi.org/10.3390/geosciences9040159
Received: 6 March 2019 / Revised: 27 March 2019 / Accepted: 29 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue Geological Seafloor Mapping)
  |  
PDF [8984 KB, uploaded 19 April 2019]
  |  

Abstract

Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1–5, more than 5) over 10,000 m2 areas, with the best performing model reaching an agreement with the human interpretation of 90%. View Full-Text
Keywords: acoustic backscatter; habitat mapping; sidescan-sonar; automatic seafloor classification; neural network; Baltic Sea acoustic backscatter; habitat mapping; sidescan-sonar; automatic seafloor classification; neural network; Baltic Sea
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Feldens, P.; Darr, A.; Feldens, A.; Tauber, F. Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network. Geosciences 2019, 9, 159.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top