Oil Spill Detection Based on CBD-Net Using Marine SAR Image

Abstract

Oil spill is one of the most widespread, frequent and harmful marine pollution. Oil spill detection based on synthetic aperture radar (SAR) images detects the oil film by identifying dark spots in the 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, causing part of the oil spill to be ignored. To solve the above problems, oil contextual and boundary-supervised detection network (CBD-Net) is proposed to extract refined oil spill regions by fusing multiscale features, where the scSE attention block is used to model the global context to improve the internal consistency of oil spill regions. 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 the proposed model outperforms other comparative models, and is able to extract robust and accurate oil spill regions from complex SAR images.