Deep Learning Approach for Accurate Segmentation of Oil Spills in Marine Systems

Oil spills present critical environmental hazards, threatening marine ecosystems and necessitating fast, accurate detection for effective mitigation. Synthetic Aperture Radar (SAR) imagery has been instrumental in detecting oil spills, but manual interpretation is often inefficient and prone to errors. This study addresses the limitations of manual methods by proposing a deep learning approach for automated oil spill detection and segmentation. Utilizing a novel transfer learning-based semantic segmentation model, this research focuses on detecting oil slicks on the sea surface with higher accuracy and efficiency. The model leverages pre-trained networks and incorporates U-Net variants, including UNet++ and MultiResUNet, to optimize spatial and contextual information extraction across different scales. These modifications enable the system to accurately detect oil spills of varying shapes and sizes while reducing the need for large datasets. Trained on a dataset that includes National Oceanic and Atmospheric Administration (NOAA) imagery and augmented samples, the model demonstrates superior performance in segmentation accuracy. The proposed solution integrates cutting-edge techniques like attention gates and multi-resolution convolution blocks, leading to faster processing and more reliable detection. The findings suggest that applying advanced deep learning methods significantly enhances oil spill monitoring systems, offering a powerful tool for real-time environmental protection and response efforts.