An automatic approach for the detection of oil spill using optimized deep learning in synthetic aperture radar images

• Published in: Energy sources. Part A, Recovery, utilization, and environmental effects Recovery, utilization, and environmental effects, 2023-08, Vol.45 (3), p.6772-6787
• Main Authors: J, Senthil Murugan, V, Parthasarathy
• Format: Article
• Language: English
• Subjects: deep learning; hybrid; hyperparameter tuning; Oil spill; SAR images; swarm intelligence
• ISSN: 1556-7036; 1556-7230
• DOI: 10.1080/15567036.2023.2216153

An oil spill is a major problem for marine life. This spilled oil affects marine life and disturbs the ecosystem. To overcome this problem, SAR sensors have been utilized in marine units to find oil spills because they are free from cloudiness and sun radiation. Even though the oil spill can be detected in SAR images, the processing time and approach are complex. Several techniques have been proposed for the detection of oil spills using matching algorithms, morphological processes, and clustering. But those techniques require multiple processing steps and algorithms for detection. It also depends on user values for detection. Hence, in this paper, the detection of the oil spill in SAR images is automated by utilizing an optimized deep-learning approach. The deep learning approach automatically detects the spilled region using a trained network. Here, the hybrid Moth flame and particle swarm algorithms are used for finding the hyperparameters for training the deep learning network. With this optimized deep learning approach, the oil spill can be detected automatically and its performance verified by implementing the proposed approach in the MATLAB R2020b version. The proposed method's performance is evaluated and compared with the existing technique using accuracy, sensitivity, specificity, Jaccard, and dice. The proposed method produces 99.64% accuracy, which is very high, and only 0.2 classification error, which is very less compared to the existing methods.