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.

由于船舶的随机方向和雷达信号引起的离散外观，合成孔径雷达 (SAR) 中的船舶检测是一项具有挑战性的任务。在本文中，我们通过采用基于上下文保留区域的对比学习，介绍了一种新的无监督域自适应框架，用于 SAR 图像中的船舶检测。我们通过从标记的遥感光学图像域和未标记的 SAR 图像域中学习知识来增强 SAR 中的船舶检测。此外，我们提出了一个伪特征生成网络来生成伪域样本以增强伪特征。具体来说，我们通过计算从对象区域和背景区域提取的特征的基于区域的对比损失来细化伪特征，以捕获用于 SAR 船舶检测的上下文信息。