Automated oil spill detection using deep learning and SAR satellite data: a case study of the Lower Congo Basin

Spaceborne Synthetic Aperture Radar (SAR) is a remote sensing sensor mounted on satellite platforms, offering image data with a wide spatial detection range and advantages such as all-weather, all-day observation, and low sensitivity to clouds and fog—making it ideal for marine oil spill monitoring. Sentinel-1A satellite has repeatedly detected oil slicks in SAR images of the Lower Congo Basin, a region with frequent natural oil and gas seepage. To address the limitations of existing segmentation models (e.g., blurred edge prediction and poor discrimination between oil spills and look-alikes) in natural oil spill detection, this study proposes a novel semantic segmentation model, NOS-Net, based on the U-Net architecture. The model deeply integrates the encoder-decoder structure of U-Net with the residual learning mechanism of ResNet, effectively solving the gradient vanishing/explosion problem in deep network training and enhancing the extraction of discriminative features. A lightweight Residual Convolutional Block Attention Module (RCBA) is innovatively introduced into the encoder’s bottleneck residual blocks, which adaptively learns feature channel importance weights via channel attention and focuses on oil spill-related spatial regions via spatial attention—strengthening feature representation while maintaining low computational overhead. Experimental validation was conducted on 110 preprocessed Sentinel-1 SAR images (1,250 × 650 × 3, subjected to radiometric calibration, 7 × 7 median filtering, and linear transformation). Results show that the proposed NOS-Net achieves an Intersection over Union (IoU) of 61.27% for oil spills and a mean IoU (mIoU) of 70.29% for five marine target categories (Oil Spill, Look-alike, Ship, Land, Sea Surface), outperforming the U-Net model with ResNet101 as the backbone by 7.48% and 5.32%, respectively.