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http://dx.doi.org/10.7780/kjrs.2022.38.6.2.4

Detection of Marine Oil Spills from PlanetScope Images Using DeepLabV3+ Model  

Kang, Jonggu (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Youn, Youjeong (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kim, Geunah (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Park, Ganghyun (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Choi, Soyeon (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Yang, Chan-Su (Marine Security and Safety Research Center, Korea Institute of Ocean Science and Technology)
Yi, Jonghyuk (SELab Inc.)
Lee, Yangwon (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_2, 2022 , pp. 1623-1631 More about this Journal
Abstract
Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.
Keywords
Oil spill detection; Deep learning; PlanetScope;
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1 Brekke, C. and A.H.S. Solberg, 2005. Oil spill detection by satellite remote sensing, Remote Sensing of Environment, 95(1): 1-13. https://doi.org/10.1016/j.rse.2004.11.015   DOI
2 Chen, L.C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation, arXiv preprint arXiv:1802.02611. https://doi.org/10.48550/arXiv.1802.02611   DOI
3 Kolokoussis, P. and V. Karathanassi, 2018. Oil spill detection and mapping using sentinel 2 imagery, Journal of Marine Science and Engineering, 6(1): 4. https://doi.org/10.3390/jmse6010004   DOI
4 Krestenitis, M., G. Orfanidis, K. Ioannidis, K. Avgerinakis, S. Vrochidis, and I. Kompatsiaris, 2019. Oil spill identification from satellite images using deep neural networks, Remote Sensing, 11(15): 1762. https://doi.org/10.3390/rs11151762   DOI
5 Mityagina, M. and O. Lavrova, 2016. Satellite survey of inner seas: Oil pollution in the Black and Caspian Seas, Remote Sensing, 8(10): 875. https://doi.org/10.3390/rs8100875   DOI
6 Planet Labs PBC, 2020. Planet Imagery and Archive, https://www.planet.com/products/planet-imagery, Accessed on Nov. 6, 2022.
7 Rajendran, S., P. Vethamony, F.N. Sadooni, H.A.-S. Al Kuwari, J.A. Al-Khayat, V.O. Seegobin, H. Govil, and S. Nasir, 2021. Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping, Environmental Pollution, 274: 116618. https://doi.org/10.1016/j.envpol.2021.116618   DOI
8 Aznar, F., M. Sempere, M. Pujol, R. Rizo, and M.J. Pujol, 2014. Modelling oil-spill detection with swarm drones, Abstract and Applied Analysis, 2014: 949407. https://doi.org/10.1155/2014/949407   DOI
9 Solberg, A.H.S., 2012. Remote sensing of ocean oil-spill pollution, Proceedings of the IEEE, 100(10): 2931-2945. https://doi.org/10.1109/JPROC.2012.2196250   DOI
10 Shelhamer, E., J. Long, and T. Darrell, 2017. Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651. https://doi.org/10.1109/TPAMI.2016.2572683   DOI
11 Singha, S., T.J. Bellerby, and O. Trieschmann, 2013. Satellite oil spill detection using artificial neural networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6): 2355-2363. https://doi.org/10.1109/JSTARS.2013.2251864   DOI
12 Saha, S., 2018. A Comprehensive Guide to Convolutional Neural Networks - The ELI5 way, https://towardsdatascience.com/a-comprehensive-guideto-convolutional-neural-networks-the-eli5-way3bd2b1164a53, Accessed on Nov. 6, 2022.
13 Topouzelis, K., V. Karathanassi, P. Pavlakis, and D. Rokos, 2007. Detection and discrimination between oil spills and look-alike phenomena through neural networks, ISPRS Journal of Photogrammetry and Remote Sensing, 62(4): 264-270. https://doi.org/10.1016/j.isprsjprs.2007.05.003   DOI
14 Yekeen, S.T., A.L. Balogun, and K.B.W. Yusof, 2020. A novel deep learning instance segmentation model for automated marine oil spill detection, ISPRS Journal of Photogrammetry and Remote Sensing, 167: 190-200. https://doi.org/10.1016/j.isprsjprs.2020.07.011   DOI
15 Alpers, W., B. Holt, and K. Zeng, 2017. Oil spill detection by imaging radars: Challenges and pitfalls, Remote Sensing of Environment, 201: 133-147. https://doi.org/10.1016/j.rse.2017.09.002   DOI
16 Arslan, N., 2018. Assessment of oil spills using Sentinel 1 C-band SAR and Landsat 8 multispectral sensors, Environmental Monitoring and Assessment, 190(11): 637. https://doi.org/10.1007/s10661-018-7017-4   DOI
17 Jiao, Z., G. Jia, and Y. Cai, 2019. A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles, Computers & Industrial Engineering, 135: 1300-1311. https://doi.org/10.1016/j.cie.2018.11.008   DOI
18 LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning, Nature, 521: 436-444. https://doi.org/10.1038/nature14539   DOI
19 Park, S.H., H.S. Jung, and M.J. Lee, 2020. Oil spill mapping from Kompsat-2 high-resolution image using directional median filtering and artificial neural network, Remote Sensing, 12(2): 253. https://doi.org/10.3390/rs12020253   DOI
20 Zhao, J., M. Temimi, H. Ghedira, and C. Hu, 2014. Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters a case study in the Arabian Gulf, Optics Express, 22(11): 13755-13772. https://doi.org/10.1364/OE.22.013755   DOI
21 Odonkor, P., Z. Ball, and S. Chowdhury, 2019. Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping, Swarm and Evolutionary Computation, 46: 52-68. https://doi.org/10.1016/j.swevo.2019.01.005   DOI