Context-Preserving Region-Based Contrastive Learning Framework for Ship Detection in SAR

Ship detection in Synthetic Aperture Radar (SAR) is a challenging task due to the random orientation of the ship and discrete appearance caused by radar signal. In this paper, We introduce a novel unsupervised domain adaptation framework for ship detection in SAR images by employing context-preserving region-based contrastive learning. We enhance the ship detection in SAR by learning knowledge from both labeled remote sensing optical image domain and unlabeled SAR image domain. Additionally, we propose a pseudo feature generation network to generate pseudo domain samples for augmenting pseudo-features. Specifically, we refine the pseudo-features by calculating a region-based contrastive loss on the features extracted from the object region and the background region to capture the contextual information for SAR ship detection. Extensive experiments and visualizations show that our method can outperform the state-of-the-art and have good generalization performance.