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Oil spills have caused serious harm to the marine environment. Remote sensing technology is one of the important tools for marine environment monitoring. Synthetic aperture radar (SAR) has become an important technology for detecting marine pollution. Identifying dark spots is essential for oil spill detection based on SAR images. Dark spots’ detection can be achieved using image segmentation techniques. However, natural phenomena, such as waves and currents, can also cause dark spots, resulting in consistently uneven intensity, high noise, and blurred boundaries in oil spill images. In addition, existing oil spill detection models often perform well for large targets but have poor detection accuracy for small targets. To solve the above problems, the oil spill contextual and boundary-supervised detection network (CBD-Net) is proposed to extract refined oil spill regions by fusing multiscale features. To improve the internal consistency of oil spill regions, the spatial and channel squeeze excitation (scSE) block is introduced. In CBD-Net, boundary details are enhanced with optimized edge supervision. In addition, a manually labeled dataset is proposed, Deep-SAR Oil Spill (SOS) dataset, aiming to solve the problem of insufficient existing oil spill detection dataset. Experimental results demonstrate that CBD-Net outperforms other comparative models and is able to extract robust and accurate oil spill regions from complex SAR images. The highest mIoU of 83.42% and the highest F1 score of 87.87% were achieved on the SOS dataset. The CBD-Net model proposed in this article can play a guiding role in the marine oil spill decision support system.
Zhu et al. (Wed,) studied this question.