Volume 11 Issue 4
Aug.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(4): 581-599
XU Congan, SU Hang, LI Jianwei, et al. RSDD-SAR: Rotated ship detection dataset in SAR images[J]. Journal of Radars, 2022, 11(4): 581–599. doi: 10.12000/JR22007
Citation: XU Congan, SU Hang, LI Jianwei, et al. RSDD-SAR: Rotated ship detection dataset in SAR images[J]. Journal of Radars, 2022, 11(4): 581–599. doi: 10.12000/JR22007
shu

RSDD-SAR: Rotated Ship Detection Dataset in SAR Images

doi: 10.12000/JR22007
  • 1.

    Naval Aviation University, Yantai 264000, China

  • 2.

    Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China

  • 3.

    Troops of 92877, Zhoushan 316000, China

  • 4.

    Wuhan University, Wuhan 430000, China

Funds:  The National Natural Science Foundation of China (61790550, 61790554, 61971432, 62022092), The Young Elite Scientists Sponsorship Program by CAST (2020-JCJQ-QT-011), The Taishan Scholar Project of Shandong Province (tsqn201909156)
More Information
  • Corresponding author: SU Hang, shpersonal_email@163.com; LI Jianwei, lgm_jw@163.com
  • Received Date: 2022-01-09
  • Rev Recd Date: 2022-05-17
    • Available Online: 2022-05-25
  • Publish Date: 2022-06-08
    Abstract
  • This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR.

     

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