Volume 8 Issue 6
Dec.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(6): 852-863
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
Citation: 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
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AIR-SARShip-1.0: High-resolution SAR Ship Detection Dataset (in English)

doi: 10.12000/JR19097
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    Key Laboratory of Network Information System Technology(NIST), Aerospace InformationResearch Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds:  The National Natural Science Foundation of China (61725105, 41801349, 41701508), National Major Project on High Resolution Earth Observation System (GFZX0404120405)
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  • Author Bio:

    SUN Xian was born in 1981. He is a researcher and doctoral supervisor at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research fields are computer vision and remote sensing image interpretation. E-mail: sunxian@mail.ie.ac.cn

    WANG Zhirui was born in 1990. He received his PhD from Tsinghua University in 2018. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research field is intelligent interpretation of SAR images. E-mail: zhirui1990@126.com

    SUN Yuanrui was born in 1995. He received his bachelor’s degree in engineering from China University of Geosciences (Wuhan) in 2017. He is now a doctoral candidate in information and communication engineering of the University of Chinese Academy of Sciences. His main research field is SAR ship detection. E-mail: sunyuanrui17@mails.ucas.ac.cn

    DIAO Wenhui was born in 1988. He received his PhD from the University of Chinese Academy of Sciences in 2016. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research interests include deep learning theory and its application in remote sensing image interpretation. E-mail: whdiao@mail.ie.ac.cn

    ZHANG Yue was born in 1990. He received his PhD from the University of Chinese Academy of Sciences in 2017. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research field is intelligent analysis and interpretation of SAR images. E-mail: zhangyue@air.cas.ac.cn

    FU Kun was born in 1974. He is a researcher and doctoral supervisor. He is the president assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences, and the director of the Key Laboratory of the Chinese Academy of Sciences. He is mainly engaged in research in the fields of geospatial data analysis and mining, and remote sensing image intelligent interpretation. He has successively won the National Science and Technology Progress Award, the first prize of the National Science and Technology Progress Award, and the first prize of the Provincial and Ministerial-Level Award. E-mail: fukun@mail.ie.ac.cn

  • Corresponding author: SUN Xian, sunxian@mail.ie.ac.cn
  • Received Date: 2019-11-16
  • Rev Recd Date: 2019-12-17
    • Available Online: 2019-12-27
  • Publish Date: 2019-12-01
    Abstract

  • Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset.

     

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