Next Article in Journal
Automated Stone Detection on Side-Scan Sonar Mosaics Using Haar-Like Features
Previous Article in Journal
Uplift Evidences Related to the Recession of Groundwater Abstraction in a Pyroclastic-Alluvial Aquifer of Southern Italy
Article Menu

Export Article

Open AccessArticle

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

1
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, 26500 Rion, Greece
2
NATO Science and Technology Organization, Centre for Maritime Research and Experimentation, 19126 La Spezia, Italy
*
Authors to whom correspondence should be addressed.
Geosciences 2019, 9(5), 214; https://doi.org/10.3390/geosciences9050214
Received: 28 March 2019 / Revised: 30 April 2019 / Accepted: 8 May 2019 / Published: 11 May 2019
  |  
PDF [10420 KB, uploaded 11 May 2019]
  |  

Abstract

Bottom trawl footprints are a prominent environmental impact of deep-sea fishery that was revealed through the evolution of underwater remote sensing technologies. Image processing techniques have been widely applied in acoustic remote sensing, but accurate trawl-mark (TM) detection is underdeveloped. The paper presents a new algorithm for the automatic detection and spatial quantification of TMs that is implemented on sidescan sonar (SSS) images of a fishing ground from the Gulf of Patras in the Eastern Mediterranean Sea. This method inspects any structure of the local seafloor in an environmentally adaptive procedure, in order to overcome the predicament of analyzing noisy and complex SSS images of the seafloor. The initial preprocessing stage deals with radiometric inconsistencies. Then, multiplex filters in the spatial domain are performed with multiscale rotated Haar-like features through integral images that locate the TM-like forms and additionally discriminate the textural characteristics of the seafloor. The final TMs are selected according to their geometric and background environment features, and the algorithm successfully produces a set of trawling-ground quantification values that could be established as a baseline measure for the status assessment of a fishing ground. View Full-Text
Keywords: trawl-marks; fishing grounds; sidescan sonar; automatic detection; Haar-like features; morphological operations; seafloor characterization trawl-marks; fishing grounds; sidescan sonar; automatic detection; Haar-like features; morphological operations; seafloor characterization
Figures

Graphical abstract

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Gournia, C.; Fakiris, E.; Geraga, M.; Williams, D.P.; Papatheodorou, G. Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations. Geosciences 2019, 9, 214.

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