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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)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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

References

  1. 1.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv preprint arXiv:1606.00915 (2016)
  2. 2.
    Cococcioni, M., Corucci, L., Masini, A., Nardelli, F.: SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images. Ocean Dyn. 62(3), 449–467 (2012)CrossRefGoogle Scholar
  3. 3.
    Fingas, M., Brown, C.: Review of oil spill remote sensing. Mar. Pollut. Bull. 83(1), 9–23 (2014)CrossRefGoogle Scholar
  4. 4.
    Giusti, A., Ciresan, D.C., Masci, J., Gambardella, L.M., Schmidhuber, J.: Fast image scanning with deep max-pooling convolutional neural networks. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 4034–4038. IEEE (2013)Google Scholar
  5. 5.
    Gonzalez, C., Sánchez, S., Paz, A., Resano, J., Mozos, D., Plaza, A.: Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing. Integr. VLSI J. 46(2), 89–103 (2013)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10578-9_23CrossRefGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  8. 8.
    Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets, pp. 286–297. Springer, Heidelberg (1990).  https://doi.org/10.1007/978-3-642-75988-8_28CrossRefGoogle Scholar
  9. 9.
    Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)Google Scholar
  10. 10.
    Konik, M., Bradtke, K.: Object-oriented approach to oil spill detection using envisat ASAR images. ISPRS J. Photogram. Remote Sens. 118, 37–52 (2016)CrossRefGoogle Scholar
  11. 11.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10602-1_48CrossRefGoogle Scholar
  12. 12.
    Mastin, G.A., Manson, J., Bradley, J., Axline, R., Hover, G.: A comparative evaluation of SAR and SLAR. Technical report, Sandia National Labs., Albuquerque, NM (United States) (1993)Google Scholar
  13. 13.
    Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., Kompatsiaris, I.: A deep neural network for oil spill semantic segmentation in SAR images. In: Accepted for presentation in IEEE International Conference on Image Processing. IEEE (2018)Google Scholar
  14. 14.
    Shen, H.Y., Zhou, P.C., Feng, S.R.: Research on multi-angle near infrared spectral-polarimetric characteristic for polluted water by spilled oil. In: International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, vol. 8193, p. 81930M. International Society for Optics and Photonics (2011)Google Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  16. 16.
    Singha, S., Bellerby, T.J., Trieschmann, O.: Satellite oil spill detection using artificial neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(6), 2355–2363 (2013)CrossRefGoogle Scholar
  17. 17.
    Solberg, A.H., Brekke, C., Husoy, P.O.: Oil spill detection in radarsat and envisat SAR images. IEEE Trans. Geosci. Remote Sens. 45(3), 746–755 (2007)CrossRefGoogle Scholar
  18. 18.
    Topouzelis, K., Psyllos, A.: Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS J. Photogram. Remote Sens. 68, 135–143 (2012)CrossRefGoogle Scholar
  19. 19.
    Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. IEEE (2015)Google Scholar

Copyright information

© 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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