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基于U-net的紫外极光观测极光卵形态提取

王梓涵 佟继周 邹自明 钟佳 白曦

王梓涵, 佟继周, 邹自明, 钟佳, 白曦. 基于U-net的紫外极光观测极光卵形态提取[J]. 空间科学学报, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667
引用本文: 王梓涵, 佟继周, 邹自明, 钟佳, 白曦. 基于U-net的紫外极光观测极光卵形态提取[J]. 空间科学学报, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667
WANG Zihan, TONG Jizhou, ZOU Ziming, ZHONG Jia, BAI Xi. Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation[J]. Chinese Journal of Space Science, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667
Citation: WANG Zihan, TONG Jizhou, ZOU Ziming, ZHONG Jia, BAI Xi. Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation[J]. Chinese Journal of Space Science, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667

基于U-net的紫外极光观测极光卵形态提取

doi: 10.11728/cjss2021.04.667
基金项目: 

中国科学院“十三五”信息化建设专项(XXH13505-04)和北京市科技计划空间科学大数据管理与应用服务平台建设课题项目(Z181100002918002)共同资助

详细信息
    作者简介:

    王梓涵,E-mail:wangzihan17@mails.ucas.ac.cn

    通讯作者:

    佟继周,E-mail:tongjz@nssc.ac.cn

  • 中图分类号: P353

Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation

  • 摘要: 极光卵形态提取是极光研究的重要手段.如何提高强干扰背景下的紫外极光图像极光卵形态提取精度,目前仍是一个难题.本文提出一种基于深度学习语义分割模型U-net的方法,实现了对极光卵形态的高精度提取.在Polar卫星紫外极光观测数据的实验结果表明,该方法相比于已有算法精度更高,对完整型极光卵和缺口型极光卵图像均能得到更加精确的提取结果,特别是针对强日辉干扰、灰度不均匀和对比度低情况下的紫外极光图像时,该方法显示了明显优势.

     

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出版历程
  • 收稿日期:  2020-02-11
  • 修回日期:  2020-12-27
  • 刊出日期:  2021-07-15

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