Dataset and Benchmark for Fine-Grained Ship Recognition in Complex Optical Remote Sensing Scenarios

Ship recognition in remote sensing imagery is crucial for numerous applications such as monitoring maritime security, preventing illegal activities and implementing environmental protection. However, most existing research rarely focuses on both the complexity of the scene and the fine-grained recognition of targets, which limits the accuracy and applicability of ship recognition technology. To propel advancements in ship recognition methodology, we propose a new dataset named Fine-Grained Ship Recognition in Complex Scenarios (FGSRCS), which contains 17 typical categories of large and medium-sized ships from 280 widely distributed ports. For the richness of the image characteristics, the dataset images are collected from multiple satellite platforms, such as Ikonos, Jilin-1, OrbView, Pleiades and WorldView, and the time span of the data covers the past 20 years. To ensure the diversity of scenarios, the dataset mainly considers five complex scenarios, including thick clouds, mists, shadows, sea clutter and ground facilities, which helps to train and improve the algorithm applicability to practical application scenarios. Furthermore, we conduct experiments with ten state-of-the-art recognition algorithms on FGSRCS dataset, providing a benchmark for algorithm application. The research result can furnish both theoretical insights and practical guidance for the development of future ship recognition models. The dataset is available on https://github.com/dwddw/FGSRCS.