[2512.18503] NASTaR: NovaSAR Automated Ship Target Recognition Dataset

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.18503 (cs) [Submitted on 20 Dec 2025 (v1), last revised 6 Apr 2026 (this version, v3)] Title:NASTaR: NovaSAR Automated Ship Target Recognition Dataset Authors:Benyamin Hosseiny, Kamirul Kamirul, Odysseas Pappas, Alin Achim View a PDF of the paper titled NASTaR: NovaSAR Automated Ship Target Recognition Dataset, by Benyamin Hosseiny and 3 other authors View PDF HTML (experimental) Abstract: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 ...