SAR TARGET EXTRACTION BASED ON SALIENCY-GUIDED CROSS-DOMAIN DISCREPANCY ALIGNMENT STRATEGY

Target extraction based on deep learning approaches is a significant task in Synthetic Aperture Radar (SAR) image processing. However, the lack of SAR image samples brings great challenges to the data-driven method. In this paper, a Saliency-guided Cross-domain Discrepancy Alignment strategy is proposed to solve this problem. Firstly, we propose a saliency-guided attention module, which utilizes the context-aware saliency knowledge to guide the feature extraction and improve the training efficiency. Secondly, we train the Saliency-guided Cross-domain Alignment Network (SCANet) by large-scale natural optical image dataset and tiny-scale SAR image dataset. Thirdly, based on the guidance of saliency attention, cross-domain representation alignment strategy is proposed to learn a latent representation which aligns feature distribution between the source and target domain. Finally, SCANet is more adaptive for SAR images and extracts targets more accurately. Comparison with state-of-the-arts and ablation experiments demonstrate the efficiency of our method, especially in complex conditions.