An Approach of Sea Clutter Suppression for SAR Images by Self-Supervised Complex-Valued Deep Learning

Strong reflections from the marine surface reduce the contrast between the target of interest and the background in synthetic aperture radar (SAR) images and severely affect the interpretation of the image. This letter proposes a framework of SAR sea clutter suppression based on a new self-supervised training strategy referred to as Clutter2Clutter (C2C), which mines self-supervised information from a large number of unlabeled SAR patches for network training. This letter also proposes a complex-valued UNet++ (CV-UNet++) network model to make full use of both amplitude and phase information of the complex SAR image, and the C2C strategy is used to train the CV-UNet++ for sea clutter suppression. Experiments on GF-3 and TerraSAR-X SAR data show that the proposed method has a better effect on suppressing sea clutter and is able to preserve the target-of-interest energy well.