SAR marine oil spill detection based on an encoder-decoder network

References

• 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.
   Web of Science ®Google Scholar
• Benedetti, P., M. Femminella, and G. Reali. 2023. “Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures.” Applied Sciences 13:1. https://doi.org/10.3390/app13010329.
   Google Scholar
• Chaturvedi, S. K., S. Banerjee, and S. Lele. 2020. “An Assessment of Oil Spill Detection Using Sentinel 1 SAR-C Images.” Journal of Ocean Engineering & Science 5 (2): 116–135. https://doi.org/10.1016/j.joes.2019.09.004.
   Web of Science ®Google Scholar
• Chen, H., and Q. Zou. 2018. “Characteristics of Wave Breaking and Blocking by Spatially Varying Opposing Currents.” Journal of Geophysical Research Oceans 123. https://doi.org/10.1029/2017JC013440.
   Web of Science ®Google Scholar
• Chollet, F. 2017. “Xception: Deep Learning with Depthwise Separable Convolutions.” In Paper presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.
   Google Scholar
• Guo, H., G. Wei, and A. Jubai. 2018. “Dark Spot Detection in SAR Images of Oil Spill Using Segnet.” Applied Sciences 8 (12). https://doi.org/10.3390/app8122670.
   Google Scholar
• Hao, Z., Y. Jiang, Y. Huihua, and H.-D. Chiang. 2021. “Adaptive Learning Rate and Momentum for Training Deep Neural Networks.” Paper presented at the Machine Learning and Knowledge Discovery in Databases. Research Track, Cham
   Google Scholar
• Huang, X., B. Zhang, W. Perrie, L. Yingcheng, and C. Wang. 2022. “A Novel Deep Learning Method for Marine Oil Spill Detection from Satellite Synthetic Aperture Radar Imagery.” Marine Pollution Bulletin 179:113666. https://doi.org/10.1016/j.marpolbul.2022.113666.
   PubMed Web of Science ®Google Scholar
• Huby, A. A., R. Sagban, and R. Alubady. 2022. “Oil Spill Detection Based on Machine Learning and Deep Learning: A Review.” In Paper presented at the 2022 5th International Conference on Engineering Technology and its Applications (IICETA), 31 May-1 June 2022, Al-Najaf, Iraq.
   Google Scholar
• Jiang, T., W. Shao, Y. Hu, G. Zheng, and W. Shen. 2023. “L-Band Analysis of the Effects of Oil Slicks on Sea Wave Characteristics.” J. Ocean Univ. China 22 (1): 9–20. https://doi.org/10.1007/s11802-023-5172-x.
   Web of Science ®Google Scholar
• Kim, D., and H.-S. Jung. 2018. “Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007.” Sensors 18:7. https://doi.org/10.3390/s18072237.
   Web of Science ®Google Scholar
• Liu, D., and L. Zhu. 2014. “Assessing China׳s Legislation on Compensation for Marine Ecological Damage: A Case Study of the Bohai Oil Spill.” Marine Policy 50:18–26. https://doi.org/10.1016/j.marpol.2014.05.009.
   Web of Science ®Google Scholar
• Li, G., J. Zhang, M. Zhang, W. Ruixia, X. Cao, and W. Liu. 2022. “Efficient depthwise separable convolution accelerator for classification and UAV object detection.” Neurocomputing 490:1–16. https://doi.org/10.1016/j.neucom.2022.02.071.
   Web of Science ®Google Scholar
• Lu, G., W. Zhang, and Z. Wang. 2022. “Optimizing Depthwise Separable Convolution Operations on GPUs.” IEEE Transactions on Parallel and Distributed Systems 33 (1): 70–87. https://doi.org/10.1109/TPDS.2021.3084813.
   Web of Science ®Google Scholar
• Mahmoud, A. S., S. A. Mohamed, R. A. El-Khoriby, H. M. AbdelSalam, and I. A. El-Khodary. 2023. “Oil Spill Identification Based on Dual Attention UNet Model Using Synthetic Aperture Radar Images.” The Journal of the Indian Society of Remote Sensing 51 (1): 121–133. https://doi.org/10.1007/s12524-022-01624-6.
   Web of Science ®Google Scholar
• Matkan, A., M. Hajeb, and Z. Azarakhsh. 2013. “Oil Spill Detection from SAR Image Using SVM Based Classification.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40:55–60. https://doi.org/10.5194/isprsarchives-XL-1-W3-55-2013.
   Google Scholar
• Ma, X., J. Xu, P. Wu, and P. Kong. 2022. “Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information from Sentinel-1 SAR Images.” IEEE Transactions on Geoscience & Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2021.3126175.
   Web of Science ®Google Scholar
• Mdakane, L. W., and W. Kleynhans. 2022. “Feature Selection and Classification of Oil Spill from Vessels Using Sentinel-1 Wide–Swath Synthetic Aperture Radar Data.” IEEE Geoscience & Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2020.3025641.
   Web of Science ®Google Scholar
• Murphy, D., B. Gemmell, L. Vaccari, L. Cheng, H. Bacosa, M. Evans, C. Gemmell, T. Harvey, M. Jalali, and T. H. R. Niepa. 2016. “An In-Depth Survey of the Oil Spill Literature Since 1968: Long Term Trends and Changes Since Deepwater Horizon.” Marine Pollution Bulletin 113 (1): 371–379. https://doi.org/10.1016/j.marpolbul.2016.10.028.
   PubMed Web of Science ®Google Scholar
• Musumeci, F., C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore. 2019. “An Overview on Application of Machine Learning Techniques in Optical Networks.” IEEE Communications Surveys & Tutorials 21 (2): 1383–1408. https://doi.org/10.1109/COMST.2018.2880039.
   Web of Science ®Google Scholar
• Nunziata, F., A. Gambardella, and M. Migliaccio. 2013. “On the Degree of Polarization for SAR Sea Oil Slick Observation.” Isprs Journal of Photogrammetry & Remote Sensing 78:41–49. https://doi.org/10.1016/j.isprsjprs.2012.12.007.
   Web of Science ®Google Scholar
• Padmapriya, T., T. Kalaiselvi, and K. Somasundaram. 2021. “Adaptive Learning Rate-Based Convolutional Neural Network Models for Brain Tumor Images Classification.” Paper presented at the Mathematical Modelling and Computational Intelligence Techniques, Singapore
   Google Scholar
• Rbah, Y., M. Mahfoudi, Y. Balboul, M. Fattah, S. Mazer, M. Elbekkali, and B. Bernoussi. 2022. “Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMt: A Survey.” In Paper presented at the 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco.
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Paper presented at the Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham
   Google Scholar
• Shaban, M., R. Salim, H. Abu Khalifeh, A. Khelifi, A. Shalaby, S. El-Mashad, A. Mahmoud, M. Ghazal, and A. El-Baz. 2021. “A Deep-Learning Framework for the Detection of Oil Spills from SAR Data.” Sensors 21:7. https://doi.org/10.3390/s21072351.
   Google Scholar
• Shereena, V. B., and G. Raju. 2022. “Medical Ultrasound Image Segmentation Using U-Net Architecture.” Paper presented at the Advances in Computing and Data Sciences, Cham
   Google Scholar
• Siddique, N., P. Sidike, C. Elkin, and V. Devabhaktuni. 2021. “U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications.” IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2021.3086020.
   Google Scholar
• Singha, S., T. Bellerby, and O. Trieschmann. 2013. “Satellite Oil Spill Detection Using Artificial Neural Networks.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 6:2355–2363. https://doi.org/10.1109/JSTARS.2013.2251864.
   Web of Science ®Google Scholar
• Skrunes, S., C. Brekke, and T. Eltoft. 2012. “Oil spill characterization with multi-polarization C- and X-band SAR.” Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.
   Google Scholar
• Solberg, A., C. Brekke, and P. Husoy. 2007. “Oil Spill Detection in Radarsat and Envisat SAR Images.” Geoscience and Remote Sensing, IEEE Transactions On 45:746–755. https://doi.org/10.1109/TGRS.2006.887019.
   Web of Science ®Google Scholar
• Wang, J., S. Ke, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, et al. 2020. “Deep High-Resolution Representation Learning for Visual Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2020.2983686.
   Google Scholar
• Xie, D.-M., Q.-P. Zou, and J. W. Cannon. 2016. “Application of SWAN+ADCIRC to Tide-Surge and Wave Simulation in Gulf of Maine During Patriot’s Day Storm.” Water Science & Engineering 9 (1): 33–41. https://doi.org/10.1016/j.wse.2016.02.003.
   Google Scholar
• Yang, T. 2015. “Dynamic Assessment of Environmental Damage Based on the Optimal Clustering Criterion – Taking Oil Spill Damage to Marine Ecological Environment as an Example.” Ecological Indicators 51:53–58. https://doi.org/10.1016/j.ecolind.2014.09.033.
   Web of Science ®Google Scholar
• Yang, Y.-J., S. Singha, and R. Mayerle. 2022. “A Deep Learning Based Oil Spill Detector Using Sentinel-1 SAR Imagery.” International Journal of Remote Sensing 43 (11): 4287–4314. https://doi.org/10.1080/01431161.2022.2109445.
   Web of Science ®Google Scholar
• Yekeen, T., Shamsudeen, and A.-L. Balogun. 2020. “Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment”. Remote Sensing 12(20). https://doi.org/10.3390/rs12203416.
   Web of Science ®Google Scholar
• Yekeen, T., Shamsudeen, A. Balogun, and K. B. Wan Yusof. 2020. “A novel deep learning instance segmentation model for automated marine oil spill detection.” Isprs Journal of Photogrammetry & Remote Sensing 167:190–200. https://doi.org/10.1016/j.isprsjprs.2020.07.011.
   Web of Science ®Google Scholar
• Zhang, S., J. Xing, X. Wang, and J. Fan. 2022. “Improved YOLOX-S Marine Oil Spill Detection Based on SAR Images.” Paper presented at the 2022 12th International Conference on Information Science and Technology (ICIST), Kaifeng, China.
   Google Scholar
• Zhu, Q., Y. Zhang, Z. Li, X. Yan, Q. Guan, Y. Zhong, L. Zhang, and D. Li. 2022. “Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images.” IEEE Transactions on Geoscience & Remote Sensing 60:1–10. https://doi.org/10.1109/TGRS.2021.3115492.
   Web of Science ®Google Scholar