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

The oil spill detection of synthetic aperture radar (SAR) images has 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. Specially, the proposed DS-Unet consists of two modules, an edge feature extraction module for extracting the local information and an Inter-scale Alignment module for capturing the global information. Moreover, an edge extraction branch is applied for handling 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.