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Open AccessArticle

Oil Spill Identification from Satellite Images Using Deep Neural Networks

Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece
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Remote Sens. 2019, 11(15), 1762; https://doi.org/10.3390/rs11151762
Received: 28 June 2019 / Revised: 12 July 2019 / Accepted: 23 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Oil Spill Remote Sensing)
Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing. View Full-Text
Keywords: oil spill detection; SAR imagery; deep convolutional neural networks; semantic image segmentation; remote sensing oil spill detection; SAR imagery; deep convolutional neural networks; semantic image segmentation; remote sensing
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MDPI and ACS Style

Krestenitis, M.; Orfanidis, G.; Ioannidis, K.; Avgerinakis, K.; Vrochidis, S.; Kompatsiaris, I. Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sens. 2019, 11, 1762. https://doi.org/10.3390/rs11151762

AMA Style

Krestenitis M, Orfanidis G, Ioannidis K, Avgerinakis K, Vrochidis S, Kompatsiaris I. Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sensing. 2019; 11(15):1762. https://doi.org/10.3390/rs11151762

Chicago/Turabian Style

Krestenitis, Marios; Orfanidis, Georgios; Ioannidis, Konstantinos; Avgerinakis, Konstantinos; Vrochidis, Stefanos; Kompatsiaris, Ioannis. 2019. "Oil Spill Identification from Satellite Images Using Deep Neural Networks" Remote Sens. 11, no. 15: 1762. https://doi.org/10.3390/rs11151762

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