NASTaR：NovaSAR自动船舶目标识别数据集
NASTaR: NovaSAR Automated Ship Target Recognition Dataset
AI总结：
本文提出NASTaR数据集，用于解决SAR图像中船舶类型分类的挑战，包含3415个船舶片段及AIS数据标注，验证了其在不同分类场景中的有效性，准确率超过87%。
摘要：
合成孔径雷达（SAR）通过捕捉和成像海上船只的强反射，提供全天候、空间基的海洋活动监测能力。该领域的一个关键挑战是船舶类型分类。由于船舶类型的高多样性和复杂性，准确识别困难，通常需要专门的深度学习模型。这些模型依赖于大量高质量的地面真实数据集以实现鲁棒性和泛化能力。此外，随着不同频率和空间分辨率的SAR卫星种类增加，对更多标注数据集的需求也增加。为此，我们提出了NovaSAR自动船舶目标识别（NASTaR）数据集。该数据集包含3415个从NovaSAR S波段图像中提取的船舶片段，标签与AIS数据匹配。它包含独特的特征，如23种独特的类别、近岸/远岸分离以及辅助的浪花数据集，用于可见船浪的片段。我们通过基准深度学习模型验证了数据集在主要船舶类型分类场景中的适用性。结果表明，对四种主要船舶类型的分类准确率超过60%，对三类场景的准确率超过70%，对货船和油轮的区分准确率超过75%，对渔船的识别准确率超过87%。NASTaR数据集可在https://doi.org/10.5523/bris.2tfa6x37oerz2lyiw6hp47058获取，相关代码用于基准测试和分析可在https://github.com/benyaminhosseiny/nastar获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
