A MODIFIED U-NET FOR OIL SPILL SEMANTIC SEGMENTATION IN SAR IMAGES

Oil spills are considered one of the major threats to the marine and coastal environment. Synthetic aperture radar (SAR) sensors are frequently employed for this purpose due to their ability to operate effectively under various weather and illumination conditions. SAR can clearly capture oil spills with distinctive radar backscatter intensity, resulting in dark regions in the images. This characteristic enables the monitoring and automatic detection of oil spills in SAR imagery. U-Net stands as one of the commonly employed semantic segmentation models, known for its ability to achieve superior segmentation performance even with limited training data. In this study, a modified lightweight U-Net model was introduced to enhance the performance of maritime multi-class segmentation in SAR images. First, a lightweight MobileNetv3 model served as the backbone for the U-Net encoder to perform feature extraction. Secondly, the convolutional block attention module (CBAM) was employed to enhance the network's capability in extracting multiscale features and to expedite the module calculation speed. The experimental results showed that the detection accuracy of the proposed method can achieve 77.07% of the mean Intersection-Over-Union ( mIOU). Compared with the original U-Net model, the proposed architecture can improve the mIOU about 4.88%.