Maritime situational awareness often relies on Automatic Identification System (AIS) transmissions to track vessel movements. However, in operational or conflict scenarios, these data may be unavailable due to signal loss, deliberate deactivation, or intentional spoofing. In such conditions, synthetic aperture radar (SAR) imagery becomes a critical sensing alternative for wide-area maritime monitoring, despite providing only static scene snapshots. This work introduces HARBOR (Heading Analysis and Reconstruction from Behavioral Observation and Radar), a complete pipeline for transforming a single SAR image into predictive motion information without requiring any auxiliary data source at inference time. The method begins with SAR image preprocessing to enhance and segment vessel candidates, followed by automatic detection, size-based classification, and heading estimation using skeleton geometry and local intensity patterns. AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters, which are then applied to generate probabilistic heatmaps of candidate future vessel positions. A case study using real COSMO-SkyMed SAR imagery demonstrates the pipeline on a maritime scene in southern Brazil, showing its ability to extract motion tendencies and generate probabilistic projections of vessel positions in data-denied environments.

翻译：海上态势感知通常依赖自动识别系统（AIS）传输数据来追踪船舶运动。然而，在作战或冲突场景中，这些数据可能因信号丢失、人为关闭或故意欺骗而不可用。在此条件下，合成孔径雷达（SAR）图像成为广域海洋监测的关键替代感知手段，但仅能提供静态场景快照。本研究提出HARBOR（基于行为观测与雷达的航向分析与重建）——一套完整流水线，可在推理阶段无需任何辅助数据源的情况下，将单幅SAR图像转换为预测性运动信息。该方法首先对SAR图像进行预处理以增强并分割船舶候选区域，随后通过骨架几何与局部强度模式实现自动检测、基于尺寸的分类及航向估计。AIS数据仅在离线标定阶段用于推导船舶类型相关的运动参数，进而生成候选未来船舶位置的概率热力图。基于真实COSMO-SkyMed SAR图像的案例研究，于巴西南部某海上场景验证了该流水线的性能，展示了其在数据缺失环境下提取运动趋势并生成船舶位置概率预测的能力。