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基于白天数据和机器学习应用程序的夜间卫星图像的云检测样本生成算法.

Xiaohang Shi1, Yulong Fan2, Lin Sun3

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China.

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|November 13, 2024
PubMed
概括
此摘要是机器生成的。

没有可见数据,精确的夜间云检测是很困难的. 这项研究使用白天卫星图像生成夜间样本,提高热红外云识别的机器学习精度.

关键词:
在GF-5 (02) 中.希马瓦里8号是什么意思轻GBM (LGB) 是一个字体.莫迪斯 (水上) 模式夜间云检测 云检测 夜间云检测热红外线 (TIR) 热红外线

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 大气科学 大气科学
  • 机器学习 机器学习

背景情况:

  • 卫星图像中的夜间云层检测具有挑战性,因为缺乏可见到近红外数据.
  • 仅使用热红外波段来区分云是很困难的,并且获得足够的夜间训练数据是不切实际的.

研究的目的:

  • 利用机器学习开发一种有效的夜间云检测方法.
  • 利用白天卫星图像生成夜间云检测样本.

主要方法:

  • 提出了一种使用白天热红外数据进行夜间云检测的新型样本生成技术.
  • 将机器学习模型应用于MODIS,GF-5 (02),和Himawari-8卫星数据.
  • 使用Lidar云产品和各种表面类型的手动标签验证结果.

主要成果:

  • 实现了比现有方法 (MYD35和夜间手动标签) 更高的准确性.
  • 具体准确度:MODIS (82.19%),GF-5 (02) (88.71%),希马瓦里-8 (79.34%) 这三种方法的准确度都比较高.
  • 性能在表面类型上有所不同,在荒地上的精度较低,但整体高.

结论:

  • 建议的白天到夜间采样生成方法有效地改善了夜间云检测.
  • 这种方法为多光谱卫星图像分析提供了一个新的视角.
  • 该方法证明了在操作性夜间云检测方面提高了准确性和实用性.