Oil Spill Pollution Automatic Detection from MultiSAR Satellite Data Using Genetic Algorithm

Marghany, Maged

doi:10.5772/58572

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Active sensors have been largely applied to monitor oil slicks and those comprised as 'slick alike' (Gade and Alpers 1999;Brekke and Solberg 2005;Hang and Duong 2009;Marghany and Hashim 2012;Marghany 2014). Oil reduces the Bragg scattering and therefore these areas look dark, although they can be subject to errors since they can be confused with organic films, grease ice, wind front areas, sectors sheltered by rocky outcrops, rain cells, internal waves or upwelling zones (Ferraro et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Validation and automatic detection of the dispersive transport of the submarine outfall of Mar del Plata, Argentina

Isla,

Cortizo,

Blotta

et al. 2024

Mar Fish Sci

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The submarine outfall of Mar del Plata city at Camet was projected considering the mean and maximum of forecasted sewage discharges, the inner-shelf depth, coliform concentration and its decay (T90) mainly induced by sunlight effect and costal salinity. In 2016 the outfall was operating with a length of 3,810 m and diffusers in the last 526 m. An economical method to monitor its performance in relation to the surroundings, is remote-sensing techniques, applying either visible or radar images. Tidal currents parallel to the coast are responsible for the transport of the sedimentary plume in the far field, after a primary dilution from a depth of 11 m. Visible images (1.5 to 6 m spatial resolution) are effective in monitoring the plume entrained in the upper portion of the water column. These analyses led to study the interaction between waves and coastal currents. Radar images (30 m resolution X and C bands) permit to survey the slick-alike plume that differs from the environment water by the surface roughness. Comparing both techniques visible images can distinguish the different colours of the plume; instead, the radar images are showing the surface roughness from the slick-alike plume. The main advantage of active sensors is that they can map the plume during a cloudy weather and even during night time.

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Section: Introductionmentioning

confidence: 99%

Validation and automatic detection of the dispersive transport of the submarine outfall of Mar del Plata, Argentina

Isla,

Cortizo,

Blotta

et al. 2024

Mar Fish Sci

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Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

Ajadi

Meyer

Tello

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Oil Spill Pollution Automatic Detection from MultiSAR Satellite Data Using Genetic Algorithm

Cited by 4 publications

References 23 publications

Validation and automatic detection of the dispersive transport of the submarine outfall of Mar del Plata, Argentina

Validation and automatic detection of the dispersive transport of the submarine outfall of Mar del Plata, Argentina

Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

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