AMDIS-Net: A Multiple Attention Mechanism Based Deep Dense Interference Suppression Network for Sentinel-1 SAR Data

The occurrence of dense interference substantially impairs the imaging performance of Sentinel-1 Synthetic Aperture Radar (SAR), leading to a notable degradation in image quality. Existing interference suppression methods are particularly susceptible to signal loss and imaging defocus, as dense interference and target echoes are heavily coupled in multi-dimensional domains. This paper introduces a deep dense interference suppression network based on a multiple attention mechanism for Sentinel-1 SAR data (AMDIS-Net). It reformulates the dense interference suppression challenge as an end-to-end mapping problem. By mapping the highly coupled dense interference and target echoes to a high-dimensional separable feature space, the AMDIS-Net effectively suppress dense interference. The AMDIS-Net leverage multiple attention mechanism to demonstrate the feature dependence of data between the local and global in frequency domain, thereby effectively mitigating dense interference and minimizing the loss of target echoes. The AMDIS-Net is trained on simulated dense interference datasets, then applied to real dense interference SAR data collected by Sentinel-1. Extensive experimental results illustrate the robust performance of the AMDIS-Net in mitigating dense interference.