大气与环境光学学报 ›› 2017, Vol. 12 ›› Issue (6): 465-.

• 光学遥感 • 上一篇    

综合高分卫星图像多维特征的云检测方法

夏雨1,2,崔生成1,杨世植1*   

  1. (1 中国科学院安徽光学精密机械研究所中国科学院大气成分与光学重点实验室,合肥 230031; 
    2 中国科学院大学,北京 100049)
  • 收稿日期:2016-04-25 修回日期:2016-07-07 出版日期:2017-11-28 发布日期:2017-11-13
  • 通讯作者: 杨世植(1963-),男,研究员,博士生导师,主要从事光学卫星遥感基础与技术、环境监测的光谱学方法和有害气体红外监测技术研究。 E-mail:szyang@aiofm.ac.cn
  • 基金资助:

    Supported by National Natural Science Foundation of China(国家自然科学基金, 41305019)

Cloud Detection Method for High Resolution Satellite Image Based on Multi-Dimensional Features

XIA Yu1,2, CUI Shengcheng1, YANG Shizhi1*   

  1. (1 Key Laboratory of Atmospheric Composition and Optical Radiation, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China;
     2 University of Chinese Academy of Sciences, Beijing 100049, China )
  • Received:2016-04-25 Revised:2016-07-07 Published:2017-11-28 Online:2017-11-13
  • Supported by:

    Supported by National Natural Science Foundation of China(国家自然科学基金, 41305019)

摘要:

云是遥感图像分析处理的一大障碍,为了解决这一问题,基于高分1号遥感影像光谱和纹理的多维特征信息,提出一种综合优化的云检测方法。针对光谱检测出的似云区域,该算法采用新的子图分割方法,结合动态阈值设置,有效提高了纹理检测的正确率。由于固定的光谱阈值设置和纹理检测都无法获取复杂环境下的云层边界信息,算法采用大津法予以修复。结果表明,该算法可有效地检测出影像中的云覆盖区域,实现薄云、厚云以及厚云边界信息的最佳提取。

关键词: 多光谱, 纹理特征, 云检测

Abstract:

Cloud is a large obstacle to remote sensing image processing and analysis. In order to solve this problem, an optimizational cloud detection algorithm is proposed for GF-1 satellite image based on the spectral and textural information. In the cloud-like areas detected by spectral analysis, a new sub-image segment method and dynamic threshold is used to improve the accuracy of texture detection. The Otsu algorithm is used to restore the thick cloud boundary information, since neither the fixed spectral threshold setting nor textural detection can get the boundaries of cloud in a complex environment. The results show that this method can effectively detect the cloud cover in the remote sensing image, optimally extract thick cloud boundary information, and effectively separate thin cloud and thick cloud.

Key words: multi-spectrum, texture features, cloud detection

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