Oil Film Semantic Segmentation Method in X-Band Marine Radar Remote Sensing Images

Effective oil-spill monitoring is critical for timely response to minimize the impact on the environment. In response to the difficulty in extracting suspected oil films from marine radar images, a semantic segmentation method was proposed. In the preprocessing of training samples in similar scene, a slicing solution was used to compensate for the small sample of original data. The U-Net semantic segmentation network was used to classify oil film into two categories: real and suspected. Existing mainstream marine radar oil-spill identification methods were compared. Experimental results demonstrate that the proposed method achieves more reliability in oil-spill semantic segmentation. It can provide real-time information for oil-spill emergency response and disaster assessment.