A two-branch marine oil spill detection model based on U-NET
To improve the accuracy of marine oil spill detection using synthetic aperture radar(SAR),an improved AW-net model based on U-NET and attention gate is proposed in this paper.In this model,the traditional single-input encoder in U-NET is replaced by a double-branch encoder and the multifeature input mode is changed from the previous feature stacking input to the texture feature and SAR gray image input in the double-branch encoder.This change is made to extract finer texture and gray information and improve the dimensionality caused by the multichannel overlay input.The multiscale texture information extracted by the double encoder is fused with the gray information using the attention gate.In the experiment,one piece of HISEA-1 SAR data was used for model training;furthermore,one piece each of HISEA-1 SAR data and Radarsat-2 SAR data was used for model testing.The oil spill detection accuracy of the two pieces of test data was better than that of other semantic segmentation models.These results demonstrate the robustness and application potential of the AW-net model.
万方数据
维普
