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) |
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 |
![]() |