通过提示基础模型实现SAR图像中的零样本船舶实例分割
Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery
AI总结：
本文提出利用通用视觉基础模型实现SAR图像中零样本船舶实例分割，通过训练YOLOv11检测器并提示SAM2生成实例掩码，有效缓解光学-SAR领域差距，实验表明方法在SSDD基准上达到高IoU和检测率。
摘要：
合成孔径雷达（SAR）在海上监视中起关键作用，但深度学习分析SAR图像受限于缺乏像素级标注。本文探讨通用视觉基础模型如何实现SAR图像中的零样本船舶实例分割，无需像素级监督。基于YOLOv11训练的检测器通过边界框定位船舶，随后提示Segment Anything Model 2（SAM2）生成实例掩码，无需任何掩码标注。与依赖微调或适配器的先前SAM-based SAR方法不同，本文方法表明仅依靠SAR训练的检测器的空间约束即可有效正则化基础模型预测。此设计部分缓解光学-SAR领域差距，使下游应用如船舶分类、尺寸估计和尾迹分析成为可能。在SSDD基准上的实验显示，平均IoU达到0.637（89%的全监督基线），总体船舶检测率为89.2%，证实了基础模型驱动SAR图像理解的可扩展性和标注高效路径。
英文摘要：
Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level annotations. This paper explores how general-purpose vision foundation models can enable zero-shot ship instance segmentation in SAR imagery, eliminating the need for pixel-level supervision. A YOLOv11-based detector trained on open SAR datasets localizes ships via bounding boxes, which then prompt the Segment Anything Model 2 (SAM2) to produce instance masks without any mask annotations. Unlike prior SAM-based SAR approaches that rely on fine tuning or adapters, our method demonstrates that spatial constraints from a SAR-trained detector alone can effectively regularize foundation model predictions. This design partially mitigates the optical-SAR domain gap and enables downstream applications such as vessel classification, size estimation, and wake analysis. Experiments on the SSDD benchmark achieve a mean IoU of 0.637 (89% of a fully supervised baseline) with an overall ship detection rate of 89.2%, confirming a scalable, annotation-efficient pathway toward foundation-model-driven SAR image understanding.
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