Multiscale Feature Fusion for Hyperspectral Marine Oil Spill Image Segmentation

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics.