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Oil Spill Identification from Satellite Images Using Deep Neural Networks
Open AccessFeature PaperArticle

Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions

1
School of Engineering, University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
2
Institute of Methodologies for Environmental Analysis (IMAA), CNR, C.da S. Loja, 85050 Tito Scalo, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2762; https://doi.org/10.3390/rs11232762
Received: 29 May 2019 / Revised: 9 September 2019 / Accepted: 21 November 2019 / Published: 23 November 2019
(This article belongs to the Special Issue Oil Spill Remote Sensing)
In recent years, the risk related to oil spill accidents has significantly increased due to a global growth in offshore extraction and oil maritime transport. To ensure sea safety, the implementation of a monitoring system able to provide real-time coverage of large areas and a timely alarm in case of accidents is of major importance. Satellite remote sensing, thanks to its inherent peculiarities, has become an essential component in such a system. Recently, the general Robust Satellite Technique (RST) approach has been successfully applied to oil spill detection (RST-OIL) using optical band satellite data. In this paper, an advanced configuration of RST-OIL is presented, and we aim to extend its applicability to a larger set of observation conditions, referring, in particular, to those in the presence of severe sun glint effects that generate some detection limits to the RST-OIL standard algorithm. To test such a configuration, the DeepWater Horizon platform accident from April 2010 was selected as a test case. We analyzed a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images that are usually significantly affected by sun glint in the Gulf of Mexico area. The accuracy of the achieved results was evaluated for comparison with a well-established satellite methodology based on microwave data, which confirms the potential of the proposed approach in identifying the oil presence on the scene with good accuracy and reliability, even in these severe conditions. View Full-Text
Keywords: oil spill; MODIS; optical band; near infrared; sun glint; DeepWater Horizon; RST-OIL oil spill; MODIS; optical band; near infrared; sun glint; DeepWater Horizon; RST-OIL
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MDPI and ACS Style

Satriano, V.; Ciancia, E.; Lacava, T.; Pergola, N.; Tramutoli, V. Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions. Remote Sens. 2019, 11, 2762.

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