Early Identification of Oil Spills in Satellite Images Using Deep CNNs

  • Marios KrestenitisEmail author
  • Georgios Orfanidis
  • Konstantinos IoannidisEmail author
  • Konstantinos Avgerinakis
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)


Oil spill pollution comprises a significant threat of the oceanic and coastal ecosystems. A continuous monitoring framework with automatic detection capabilities could be valuable as an early warning system so as to minimize the response time of the authorities and prevent any environmental disaster. The usage of Synthetic Aperture Radar (SAR) data acquired from satellites have received a considerable attention in remote sensing and image analysis applications for disaster management, due to the wide area coverage and the all-weather capabilities. Over the past few years, multiple solutions have been proposed to identify oil spills over the sea surface by processing SAR images. In addition, deep convolutional neural networks (DCNN) have shown remarkable results in a wide variety of image analysis applications and could be deployed to overcome the performance of previously proposed methods. This paper describes the development of an image analysis approach utilizing the benefits of a deep CNN combined with SAR imagery to establish an early warning system for oil spill pollution identification. SAR images are semantically segmented into multiple areas of interest including oil spill, look-alikes, land areas, sea surface and ships. The model was trained and tested using multiple SAR images, acquired from the Copernicus Open Access Hub and manually annotated. The dataset is a result of Sentinel-1 missions and EMSA records for relative pollution events. The conducted experiments demonstrate that the deployed DCNN model can accurately discriminate oil spills from other instances providing the relevant authorities a valuable tool to manage the upcoming disaster effectively.


Oil spill identification SAR image analysis Deep convolutional neural networks Disaster management 



This work was supported by ROBORDER and EOPEN projects funded by the European Commission under grant agreements No 740593 and No 776019, respectively.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marios Krestenitis
    • 1
    Email author
  • Georgios Orfanidis
    • 1
  • Konstantinos Ioannidis
    • 1
    Email author
  • Konstantinos Avgerinakis
    • 1
  • Stefanos Vrochidis
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Centre for Research and Technology Hellas, Information Techologies InstituteThessalonikiGreece

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