Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative satellite imagery and implementing a fusion approach that links visual detections with AIS data to identify dark vessels, validate cooperative traffic, and support advanced MDA. The You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel. An annotated data set of 4,550 instances was developed from $5{,}973~\mathrm{mi}^2$ of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrated vessel classification (tugboat, crane barge, bulk carrier, cargo ship, and hopper barge) with an F1 score of 95.8\%; barge cover (covered or uncovered) detection yielded an F1 score of 91.6\%; operational status (staged or in motion) classification reached an F1 score of 99.4\%. Directionality (upstream, downstream) yielded 93.8\% accuracy. The barge count estimation resulted in a mean absolute error (MAE) of 2.4 barges. Spatial transferability analysis across geographically disjoint river segments showed accuracy was maintained as high as 98\%. These results underscore the viability of integrating non-cooperative satellite sensing with AIS fusion. This approach enables near-real-time fleet inventories, supports anomaly detection, and generates high-quality data for inland waterway surveillance. Future work will expand annotated datasets, incorporate temporal tracking, and explore multi-modal deep learning to further enhance operational scalability.

翻译：内陆水道海事领域感知（MDA）仍面临协同系统脆弱性的挑战。本文提出一种新颖框架，将高分辨率卫星影像与来自自动识别系统（AIS）的船舶轨迹数据相融合。该研究通过利用非协同卫星影像并实施一种将视觉检测与AIS数据关联的融合方法，以识别"暗船"、验证协同交通并支持高级MDA，从而解决了基于AIS监测的局限性。采用You Only Look Once (YOLO) v11目标检测模型，通过船舶类型、驳船覆盖状态、运行状态、驳船数量及航行方向对船舶和驳船进行检测与特征识别。基于密西西比河下游5,973平方英里影像构建了包含4,550个标注实例的数据集。在预留测试集上的评估显示：船舶分类（拖船、起重驳船、散货船、货船、敞舱驳船）F1分数达95.8%；驳船覆盖状态（覆盖/未覆盖）检测F1分数为91.6%；运行状态（停泊/航行）分类F1分数达99.4%；航向（上行/下行）分类准确率为93.8%。驳船数量估算的平均绝对误差（MAE）为2.4艘。跨地理隔离河段的空间可迁移性分析表明准确率最高可保持98%。这些结果证实了非协同卫星传感与AIS融合的可行性。该方法支持近实时船队清单生成、异常检测辅助，并为内陆水道监控提供高质量数据。未来工作将扩展标注数据集、整合时序跟踪并探索多模态深度学习以进一步提升操作可扩展性。