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
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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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    • References(40)
  • [1]
    LI Jianwei, QU Changwen, and SHAO Jiaqi. Ship detection in SAR images based on an improved faster R-CNN[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017: 1–6.
    [2]
    ZHANG Xiaohan, WANG Haipeng, XU Congan, et al. A lightweight feature optimizing network for ship detection in SAR image[J]. IEEE Access, 2019, 7: 141662–141678. doi: 10.1109/ACCESS.2019.2943241
    [3]
    ZHANG Tianwen, ZHANG Xiaoling, SHI Jun, et al. Depthwise separable convolution neural network for high-speed SAR ship detection[J]. Remote Sensing, 2019, 11(21): 2483. doi: 10.3390/rs11212483
    [4]
    ZHANG Peng, TANG Jinsong, ZHONG Heping, et al. Self-trained target detection of radar and sonar images using automatic deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 4701914. doi: 10.1109/TGRS.2021.3096011
    [5]
    孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0: 高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852–862. doi: 10.12000/JR19097

    SUN Xian, WANG Zhirui, SUN Yuanrui, et al. AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852–862. doi: 10.12000/JR19097
    [6]
    WANG Yuanyuan, WANG Chao, ZHANG Hong, et al. A SAR dataset of ship detection for deep learning under complex backgrounds[J]. Remote Sensing, 2019, 11(7): 765. doi: 10.3390/rs11070765
    [7]
    WEI Shunjun, ZENG Xiangfeng, QU Qizhe, et al. HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 2020, 8: 120234–120254. doi: 10.1109/ACCESS.2020.3005861
    [8]
    ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. LS-SSDD-v1.0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR images[J]. Remote Sensing, 2020, 12(18): 2997. doi: 10.3390/RS12182997
    [9]
    LEI Songlin, LU Dongdong, QIU Xiaolan, et al. SRSDD-v1.0: A high-resolution SAR rotation ship detection dataset[J]. Remote Sensing, 2021, 13(24): 5104. doi: 10.3390/rs13245104
    [10]
    HUANG Lanqing, LIU Bin, LI Boying, et al. OpenSARShip: A dataset dedicated to Sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672
    [11]
    LI Boying, LIU Bin, HUANG Lanqing, et al. OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery[C]. 2017 SAR in Big Data Era: Models, Methods and Applications, Beijing, China, 2017: 1–5.
    [12]
    HOU Xiyue, AO Wei, SONG Qian, et al. FUSAR-Ship: Building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition[J]. Science China Information Sciences, 2020, 63(4): 140303. doi: 10.1007/s11432-019-2772-5
    [13]
    ZHANG Tianwen, ZHANG Xiaoling, LI Jianwei, et al. SAR ship detection dataset (SSDD): Official release and comprehensive data analysis[J]. Remote Sensing, 2021, 13(18): 3690. doi: 10.3390/rs13183690
    [14]
    European Space Agency[EB/OL]. https://www.esa.int/, 2022.
    [15]
    ITTVIS. ENVI-Image processing and analysis software solution[EB/OL]. https://www.ittvis.com/envi/, 2022.
    [16]
    PIE-SAR[EB/OL]. https://www.piesat.cn/product/PIE-SAR/index.html, 2022.
    [17]
    EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The Pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303–338. doi: 10.1007/s11263-009-0275-4
    [18]
    HE Yishan, GAO Feo, WANG Jun, et al. Learning polar encodings for arbitrary-oriented ship detection in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 3846–3859. doi: 10.1109/JSTARS.2021.3068530
    [19]
    RoLabelImg: Label rotated rect on images for training[EB/OL].https://github.com/cgvict/roLabelImg, 2022.
    [20]
    LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 740–755.
    [21]
    REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91–99.
    [22]
    REDMON J and FARHADI A. YOLO9000: Better, faster, stronger[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6517–6525.
    [23]
    REDMON J and FARHADI A. YOLOv3: An incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767, 2018.
    [24]
    BOCHKOVSKIY A, WANG C Y, and LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934, 2020.
    [25]
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
    [26]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007.
    [27]
    ZHOU Xingyi, WANG Dequan, and KRÄHENBÜHL P. Objects as points[EB/OL]. http://arxiv.org/abs/1904.07850, 2019.
    [28]
    TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS: Fully convolutional one-stage object detection[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, South Korea, 2019: 9626–9635.
    [29]
    DING Jian, XUE Nan, LONG Yang, et al. Learning RoI transformer for oriented object detection in aerial images[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 2844–2853.
    [30]
    XU Yongchao, FU Mingtao, WANG Qimeng, et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(4): 1452–1459. doi: 10.1109/TPAMI.2020.2974745
    [31]
    XIE Xingxing, CHENG Gong, WANG Jiabao, et al. Oriented R-CNN for object detection[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 3500–3509.
    [32]
    HAN Jiaming, DING Jian, XUE Nan, et al. ReDet: A rotation-equivariant detector for aerial object detection[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Montreal, Canada, 2021: 2785–2794.
    [33]
    HAN Jiaming, DING Jian, LI Jie, et al. Align deep features for oriented object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5602511. doi: 10.1109/TGRS.2021.3062048
    [34]
    YANG Xue, YAN Junchi, FENG Ziming, et al. R3Det: Refined single-stage detector with feature refinement for rotating object[C]. Thirty-Fifth AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2021.
    [35]
    GUO Zonghao, LIU Chang, ZHANG Xiaosong, et al. Beyond bounding-box: Convex-hull feature adaptation for oriented and densely packed object detection[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 8788–8797.
    [36]
    AN Quanzhi, PAN Zongxu, LIU Lei, et al. DRBox-v2: An improved detector with rotatable boxes for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8333–8349. doi: 10.1109/TGRS.2019.2920534
    [37]
    HU Shimin, LIANG Dun, YANG Guoye, et al. Jittor: A novel deep learning framework with meta-operators and unified graph execution[J]. Science China Information Sciences, 2020, 63(12): 222103. doi: 10.1007/s11432-020-3097-4
    [38]
    ZHOU Yue, YANG Xue, ZHANG Gefan, et al. MMRotate: A rotated object detection benchmark using PyTorch[EB/OL]. https://github.com/open-mmlab/mmrotate, 2022.
    [39]
    GOYAL P, DOLLÁR P, GIRSHICK R, et al. Accurate, large minibatch SGD: Training ImageNet in 1 hour[EB/OL]. https://arxiv.org/abs/1706.02677, 2018.
    [40]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
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