IRO Journals
IRO Journal on Sustainable Wireless Systems

An Integrated SAR Image Segmentation and IoT-Based Monitoring Framework for Marine Oil Spill Detection and Severity Classification

Open Access
Published: 28 October, 2026
Pages: 253-266
  • Sasikala K. , Sasikala K.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
  • Soniya P. , Soniya P.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
  • Sachin Sharma A. , Sachin Sharma A.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
  • Vigneshwaran S. , Vigneshwaran S.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
  • Harish Kumar A V. , Harish Kumar A V.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
  • Sanjeev P. Sanjeev P.
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
    Department of Information Technology, R P Sarathy Institute of Technology, Salem, India
view PDF
view PDF

How to Cite

K., Sasikala, Soniya P., Sachin Sharma A., Vigneshwaran S., Harish Kumar A V., and Sanjeev P. 2026. “An Integrated SAR Image Segmentation and IoT-Based Monitoring Framework for Marine Oil Spill Detection and Severity Classification”. IRO Journal on Sustainable Wireless Systems 8 (4): 253-66. https://doi.org/10.36548/jsws.2026.4.001.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Marine Oil-Spill Detection
Sentinel-1 SAR
U-Net
Semantic Segmentation
Spill-Area Estimation
Internet of Things
Automated Alerting

Abstract

Accurate segmentation of oil spill areas in Synthetic Aperture Radar (SAR) images is achieved by distinguishing between regions contaminated by oil and similar areas of the ocean surface. This research proposes an AI-IoT approach to segment and assess the severity of oil spillage from Sentinel-1 SAR images. The proposed methodology involves logarithmic transformation, normalization, and patch generation prior to training a U-Net convolutional neural network with ground truth segmentation masks. Performance of the network is measured on a separate test set using the Dice coefficient and Intersection over Union (IoU). An obtained Dice coefficient of 0.718 and IoU of 0.593 measure the spatial correspondence of predicted and reference areas of spills, while inference time is about two seconds per tile. Mask predictions are converted into area estimates and classified into three levels – minor, moderate, and severe levels. An IoT communication module transmits data related to spill location, severity, timestamp, and confidence factor to a cloud server for geospatial visualization and alerts. The system uses pixel-based SAR data analysis with an organized environmental monitoring and coastal response process.

References

  1. Krestenitis, Marios, Georgios Orfanidis, Konstantinos Ioannidis, Konstantinos Avgerinakis, Stefanos Vrochidis, and Ioannis Kompatsiaris. “Oil Spill Identification from Satellite Images Using Deep Neural Networks.” Remote Sensing, 2019, vol. 11, no. 15: 1762.
  2. Zakzouk, Mohamed, Abdulaziz M. Abdulaziz, Islam Abou El-Magd, Abdel Sattar Dahab, and Elham M. Ali. “Automated Oil Spill Detection Using Deep Learning and SAR Satellite Data for the Northern Entrance of the Suez Canal.” Scientific Reports, 2025, vol. 15, no. 1: 20107.
  3. Shaban, Mohamed, Reem Salim, Hadil Abu Khalifeh, Adel Khelifi, Ahmed Shalaby, Shady El-Mashad, Ali Mahmoud, Mohammed Ghazal, and Ayman El-Baz. “A Deep-Learning Framework for the Detection of Oil Spills from SAR Data.” Sensors, 2021, vol. 21, no. 7: 2351.
  4. Chen, Yi-Ting, Lena Chang, and Jung-Hua Wang. “Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection.” Sensors, 2024, vol. 24, no. 12s: 3724.
  5. Cai, Yu, Jingjing Su, Jun Song, Dekai Xu, Liankang Zhang, and Gaoyuan Shen. “LRA-UNet: A Lightweight Residual Attention Network for SAR Marine Oil Spill Detection.” Journal of Marine Science and Engineering, 2025, vol. 13, no. 6: 1161.
  6. Prajapati, Kinjal, Madhuri Bhavsar, and Alka Mahajan. “GSCAT-UNET: Enhanced U-Net Model with Spatial-Channel Attention Gate and Three-Level Attention for Oil Spill Detection Using SAR Data.” Marine Pollution Bulletin, 2025, vol. 212: 117583.
  7. Dong, Shaokang, Jiangfan Feng, Zhujun Gu, Kuan Yin, and Ying Long. “A Review of Artificial Intelligence and Remote Sensing for Marine Oil Spill Detection, Classification, and Thickness Estimation.” Remote Sensing, 2025, vol. 17, no. 22: 3681.
  8. Pham, Duc, Jayabal Chandra Priya, Lech Rowiński, Lan Huong Nguyen, Thi Anh Em Bui, Thi Thai Le, Phuoc Quy Phong Nguyen, and Nguyen Dang Khoa Pham. “Next-gen Marine Safety: Real-Time Automated Oil Spill Detection and Monitoring with IoT and Transfer Learning.” Polish Maritime Research, 2025, vol. 32, no. 4: 168-186.
  9. Xu, Guobao, Yanjun Shi, Xueyan Sun, and Weiming Shen. “Internet of Things in Marine Environment Monitoring: A Review.” Sensors, 2019, vol. 19, no. 7: 1711.
  10. R. Trujillo-Acatitla, J. Tuxpan-Vargas, C. Ovando-Vázquez, and E. Monterrubio-Martínez, “Sentinel-1 SAR Oil Spill Image Dataset for Train, Validate, and Test Deep Learning Models. Part I,” Zenodo, 2024. Available: https://zenodo.org/records/8346860
  11. R. Trujillo-Acatitla, J. Tuxpan-Vargas, C. Ovando-Vázquez, and E. Monterrubio-Martínez, “Sentinel-1 SAR Oil Spill Image Dataset for Train, Validate, and Test Deep Learning Models. Part II,” Zenodo, 2024. Available: https://zenodo.org/records/8253899
  12. R. Trujillo-Acatitla, J. Tuxpan-Vargas, C. Ovando-Vázquez, and E. Monterrubio-Martínez, “Sentinel-1 SAR Oil Spill Image Dataset for Train, Validate, and Test Deep Learning Models. Part III.” Available: https://zenodo.org/records/13761290