Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models

• Published in: Journal of marine science and engineering 2024-11, Vol.12 (11), p.2095
• Main Authors: Kang, Jonggu, Yang, Chansu, Yi, Jonghyuk, Lee, Yangwon
• Format: Article
• Language: English
• Subjects: Aircraft; Aircraft detection; Artificial neural networks; Artificial satellites in remote sensing; CNN; Damage detection; Deep learning; Drone aircraft; Electric transformers; Environmental monitoring; Image enhancement; Image resolution; Machine learning; Marine accidents; Marine ecosystems; Marine environment; Marine pollution; Neural networks; Ocean models; oil spill detection; Oil spills; optical satellite image; Pollution detection; Radar detection; Radar imaging; Remote sensing; SAR (radar); Satellite imagery; Satellites; Semantics; Sensors; Spatial discrimination learning; Synthetic aperture radar; transformer; Unmanned aerial vehicles; Unmanned aircraft
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12112095

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring.