Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due to the high diversity and complexity of ship types, accurate recognition is difficult and typically requires specialized deep learning models. These models, however, depend on large, high-quality ground-truth datasets to achieve robust performance and generalization. Furthermore, the growing variety of SAR satellites operating at different frequencies and spatial resolutions has amplified the need for more annotated datasets to enhance model accuracy. To address this, we present the NovaSAR Automated Ship Target Recognition (NASTaR) dataset. This dataset comprises of 3415 ship patches extracted from NovaSAR S-band imagery, with labels matched to AIS data. It includes distinctive features such as 23 unique classes, inshore/offshore separation, and an auxiliary wake dataset for patches where ship wakes are visible. We validated the dataset applicability across prominent ship-type classification scenarios using benchmark deep learning models. Results demonstrate over 60% accuracy for classifying four major ship types, over 70% for a three-class scenario, more than 75% for distinguishing cargo from tanker ships, and over 87% for identifying fishing vessels. The NASTaR dataset is available at https://10.5523/bris, while relevant codes for benchmarking and analysis are available at https://github.com/benyaminhosseiny/nastar.

翻译：合成孔径雷达（SAR）通过捕获和成像海上船只的强反射信号，为全天候、天基海上活动监测提供了独特能力。该领域一个明确存在的挑战是舰船类型分类。由于舰船类型的高度多样性和复杂性，精确识别较为困难，通常需要专门的深度学习模型。然而，这些模型依赖于大规模、高质量的真实标注数据集才能实现稳健的性能和泛化能力。此外，在不同频率和空间分辨率下运行的SAR卫星种类日益增多，进一步加大了对更多标注数据集的需求，以提升模型的准确性。为此，我们提出了NovaSAR自动舰船目标识别（NASTaR）数据集。该数据集包含从NovaSAR S波段影像中提取的3415个舰船图像块，其标签与AIS数据进行了匹配。它具有鲜明的特征，包括23个独特的类别、近岸/离岸分离，以及一个针对可见舰船尾迹图像块的辅助尾迹数据集。我们使用基准深度学习模型验证了该数据集在多个重要舰船类型分类场景中的适用性。结果表明，在四类主要舰船类型分类中准确率超过60%，在三分类场景中超过70%，在区分货船与油轮时超过75%，在识别渔船时超过87%。NASTaR数据集可通过 https://10.5523/bris 获取，而用于基准测试和分析的相关代码可在 https://github.com/benyaminhosseiny/nastar 获取。