TL;DR
The paper introduces the NASTaR dataset, a comprehensive SAR ship target recognition dataset with 3415 labeled ship patches, to improve deep learning models' accuracy in maritime monitoring.
Contribution
It provides a new, annotated SAR dataset with diverse ship classes and benchmarks its effectiveness for ship type classification tasks.
Findings
Achieved over 60% accuracy in four major ship type classifications.
Demonstrated over 70% accuracy in three-class scenarios.
Reached more than 87% accuracy in identifying fishing vessels.
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…
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