Next Article in Journal
Underwater Turbulence Detection Using Gated Wavefront Sensing Technique
Next Article in Special Issue
Single-Step Incubation Determination of miRNAs in Cancer Cells Using an Amperometric Biosensor Based on Competitive Hybridization onto Magnetic Beads
Previous Article in Journal
Multi-User Low Intrusive Occupancy Detection
Previous Article in Special Issue
Redox Cycling Realized in Paper-Based Biochemical Sensor for Selective Detection of Reversible Redox Molecules Without Micro/Nano Fabrication Process
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(3), 797; https://doi.org/10.3390/s18030797

Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders

1
Pattern Recognition and Artificial Intelligence Group, Department of Software and Computing Systems, University of Alicante, E-03690 Alicante, Spain
2
Automation, Robotics and Computer Vision Group, Department of Physics, Systems Engineering and Signal Theory, University of Alicante, E-03690 Alicante, Spain
3
Computer Science Research Institute, University of Alicante, E-03690 Alicante, Spain
4
School of Informatics, University of Edinburgh, EH1 2QL Edinburgh, UK
*
Author to whom correspondence should be addressed.
Received: 8 February 2018 / Revised: 27 February 2018 / Accepted: 4 March 2018 / Published: 6 March 2018
(This article belongs to the Special Issue I3S 2017 Selected Papers)
Full-Text   |   PDF [4045 KB, uploaded 9 March 2018]   |  

Abstract

In this work, we use deep neural autoencoders to segment oil spills from Side-Looking Airborne Radar (SLAR) imagery. Synthetic Aperture Radar (SAR) has been much exploited for ocean surface monitoring, especially for oil pollution detection, but few approaches in the literature use SLAR. Our sensor consists of two SAR antennas mounted on an aircraft, enabling a quicker response than satellite sensors for emergency services when an oil spill occurs. Experiments on TERMA radar were carried out to detect oil spills on Spanish coasts using deep selectional autoencoders and RED-nets (very deep Residual Encoder-Decoder Networks). Different configurations of these networks were evaluated and the best topology significantly outperformed previous approaches, correctly detecting 100% of the spills and obtaining an F 1 score of 93.01% at the pixel level. The proposed autoencoders perform accurately in SLAR imagery that has artifacts and noise caused by the aircraft maneuvers, in different weather conditions and with the presence of look-alikes due to natural phenomena such as shoals of fish and seaweed. View Full-Text
Keywords: oil spill detection; side-looking airborne radar; neural networks; supervised learning; radar detection oil spill detection; side-looking airborne radar; neural networks; supervised learning; radar detection
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Gallego, A.-J.; Gil, P.; Pertusa, A.; Fisher, R.B. Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders. Sensors 2018, 18, 797.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top