On the SAR derived alert in the detection of oil spills according to the analysis of the EGEMP

Ferraro, Guido; Baschek, Björn; Montpellier, Geraldine de; Njoten, Ove; Perkovič, Marko; Vespe, Michele

doi:10.1016/j.marpolbul.2009.08.025

Cited by 38 publications

(14 citation statements)

References 16 publications

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“…Neural network approaches can discriminate slick-like features from other dark, non-slick features, providing guidance on potential oil slicks for further investigation (Topouzelis et al, 2007). The need for confidence level classification protocols for radar-based detection has been noted; however, there is a need for contextual information, including assessment of analysis techniques, contrast, geographic location, winds and other meteorological data (Ferraro et al, 2010). These evaluations are critical to satellite utility in allocating airborne resources or indicating further satellite data analysis.…”

Section: Oil Slick Remote Sensing For Oil Spill Responsementioning

confidence: 99%

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

Leifer

Lehr²,

Simecek-Beatty³

et al. 2012

Remote Sensing of Environment

451

298

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Section: Oil Slick Remote Sensing For Oil Spill Responsementioning

confidence: 99%

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

Leifer

Lehr²,

Simecek-Beatty³

et al. 2012

Remote Sensing of Environment

451

298

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“…The Joint Research Centre and among other international cooperation EGEMP, the European Group of Experts on remote sensing monitoring of marine pollution, fostered the exchange of knowledge and the harmonisation, e.g. by compiling satellite surveillance guidelines and evaluating alert mechanisms [22] for an improved interplay of satellite and aircraft surveillance. From 2007 on, CleanSeaNet (CSN) of the European Maritime Safety Agency (EMSA) has been acting as a support service for member states for their marine pollution control.…”

Section: History and Projectsmentioning

confidence: 99%

“…simultaneous SAR imagery acquired at different polarisation combination of the transmitted and received microwaves) [31]. A review of actual classification systems, seen from a European perspective, was recently provided by Ferraro et al [22].…”

Section: Spaceborne Sensorsmentioning

confidence: 99%

“…The question how the satellite service information should be prepared for optimal operational use and for the planning of follow-up actions is subject to ongoing discussions and research [22]. Next to the operational use, the combination of satellite and aircraft is a valuable tool for validation -either of the satellite service or also for drift models -and thus for improvement of the existing tools.…”

Section: Combined Monitoring Systemmentioning

confidence: 99%

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The German Operational Monitoring System in the North Sea: Sensors, Methods and Example Data

Baschek¹,

Gade²,

Bernem³

et al. 2015

The Handbook of Environmental Chemistry

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Operational oil pollution surveillance has been performed in Germany for almost 30 years. Sophisticated state-of-the-art sensors are being used for frequent airborne surveillance, while satellite data are used as prewarning and additional information input on a routine basis. In parallel, basic research on the imaging of marine oil pollution by synthetic aperture radar (SAR) has been performed, and a basic understanding of the imaging of biogenic and anthropogenic marine surface films by active microwave sensors has been developed. In this paper, we provide an overview of the current operational surveillance system, and we give some historical background summarising some of the results of the research conducted during the past decades. Within this chapter, example images from pollution events are given for several sensors. The German coast's spatial and temporal vulnerability to oil pollution is quantified, and the use of dispersants in a highly vulnerable ecosystem such as the "Wadden Sea" is discussed.

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“…Finally, it must be observed that the interesting conclusions provided in Brekke and Solberg (2005), concerning the use of satellite SAR and the related limitations, are still valid today. As regards to SAR-based services, a recent review of them can be found in Ferraro et al (2010), in addition to the CleanSeaNet service run by EMSA mentioned above.…”

Section: Satellite Sensors For Oil Spill Detectionmentioning

confidence: 99%

SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images

et al. 2012

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This paper addresses oil spill detection from remotely sensed optical images. In particular, it focuses on the automatic classification of regions of interest (ROIs) in two classes, namely oil spills or look-alikes. Candidate regions and the corresponding boundaries have been manually identified from full resolution Moderate Resolution Imaging Spectroradiometer images, related to the Mediterranean Sea over the years 2008 and 2009. Then, a set of features has been extracted from each ROI, allowing to formulate the oil spill detection problem as a two-class classification task on the provided regions (i.e. using a supervised learning strategy). Since ROI classification is challenging, some desired characteristics for the classification algorithm are first identified, such as accuracy, robustness, etc. Then, a solution (called SVME) is provided: it is based on an ensemble of incremental/decremental cost-oriented Support Vector Machines, aggregated with the Receiving Operating Characteristic (ROC) convex hull method in the ROC space. Such a solution addresses all the desired characteristics. Finally, the results obtained on the collected dataset are shown. The importance of this study is the devising of a powerful classification technique that may have an impact on optical oil spill detection from space, especially if fused with satellite synthetic aperture radar data. Moreover, it is shown how the proposed system can be used as a decision support tool, to help a junior operator in making more reliable detections

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On the SAR derived alert in the detection of oil spills according to the analysis of the EGEMP

Cited by 38 publications

References 16 publications

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

The German Operational Monitoring System in the North Sea: Sensors, Methods and Example Data

SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images

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