开源首个基于NovaSAR的船型识别数据集，支持高精度海上目标分类。
NASTaR: NovaSAR Automated Ship Target Recognition Dataset
- 从NovaSAR S波段图像提取3415张船体补丁，匹配AIS数据实现精准标注
- 在四类船型分类中准确率超60%，渔船识别达87%以上
- 含23类船型、近海/远海区分及船尾迹辅助数据，适合遥感与深度学习研究
合成孔径雷达（SAR）可全天候、空间化监测海上活动，通过捕捉船舶强反射成像。船型分类是该领域的关键挑战，因船型多样复杂，需专用深度学习模型，而模型性能依赖大规模高质量标注数据。随着多频段、多分辨率SAR卫星增多，对标注数据的需求愈发迫切。为此，我们提出NovaSAR自动船型识别数据集（NASTaR），包含3415个来自NovaSAR S波段影像的船体补丁，标签与AIS数据匹配。数据集具23种独特船型类别，区分近海与远海场景，并附带船尾迹辅助数据集。通过基准深度学习模型验证，结果显示：四类船型分类准确率超60%，三类场景超70%，货轮与油轮区分超75%，渔船识别超87%。数据集可通过https://doi.org/10.5523/bris.2tfa6x37oerz2lyiw6hp47058获取，相关代码见https://github.com/benyaminhosseiny/nastar。
原文摘要 · Abstract (English)
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://doi.org/10.5523/bris.2tfa6x37oerz2lyiw6hp47058, while relevant codes for benchmarking and analysis are available at https://github.com/benyaminhosseiny/nastar.
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