Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-03-20
DOI
https://doi.org/10.3390/s25061927
Citations
4
Primary Topic
Oil Spill Detection and Mitigation
Type
article
Field-Weighted Citation Impact
2.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery

Lisa Landuyt, Dirk Nuyts, Marian-Daniel Iordache, Françoise Viallefont-Robinet et al.
4 citations
Sensors
Oil Spill Detection and Mitigation
2.01
article

Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery

Lisa Landuyt, Dirk Nuyts, Marian-Daniel Iordache, Françoise Viallefont-Robinet, Robrecht Moelans, Els Knaeps, Gert Strackx, Joeri Vandeperre
article en
4 citations

Abstract

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments.

SensorsVol. 25(6)
Université Fédérale de Toulouse Midi-Pyrénées (FR), Port of Antwerp (BE), Flemish Institute for Technological Research (BE)
Belgian Federal Science Policy Office
Openalex Percentile: Top 15%
Oil Spill Detection and Mitigation
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.