Oil Spill Detection on Sea Surface Based on SAR-Images by Using Global Context and Edge Perception

Synthetic Aperture Radar (SAR) has been widely used in marine environmental monitoring. However, there are some challenges in the detection of oil spills based on SAR images. Existing oil spill detection methods inadequately consider the characteristics of SAR images, such as high noise, complex texture, and blurry boundaries, resulting in poor edge accuracy and false positives and negatives in oil spill region detection. In response to these issues, this paper proposes a sea surface oil spill detection model based on global context and edge awareness. Firstly, the model enhances the extraction and utilization of edge information by designing an edge-aware detection branch network. Secondly, improvements are made to the feature extraction method of the encoder. The backbone network is enhanced for capturing low-level feature information of oil spills by introducing the CA (Channel Attention) module. The use of a dense-connected spatial pyramid module expands the receptive field to enhance global context awareness, thereby improving the extraction of high-level semantic features from oil spill features. Finally, through comparative experiments, the effectiveness of the proposed detection model in this chapter is validated.