DS-UNet: Dual-Stream U-Net for Oil Spill Detection of SAR Image

The oil spill detection of synthetic aperture radar (SAR) images has had great success. Existing deep-learning-based methods make predictions mainly based on the U-Net structure and Transformer, which fail to blend the local and global information generated by other different feature maps. In this letter, we proposed a dual-stream Unet (DS-Unet) for oil spill detection of SAR images. In particular, the proposed DS-Unet consists of two modules: an edge feature extraction module for extracting the local information and an interscale alignment module for capturing the global information. Moreover, an edge extraction branch is applied to handle the speckle noise of SAR images. Extensive experiments on two real-world datasets (Palsar and Sentinel) have shown that the proposed DS-Unet outperforms many existing state-of-the-art methods.