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Research on multispectral satellite image cloud and cloud shadow detection algorithm of domestic satellite

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：hucm@aircas.ac.cn
  • Introduction：胡昌苗，研究方向为遥感图像处理。E-mail： hucm@aircas.ac.cn
HU Changmiao,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Zheng,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：tangping@aircas.ac.cn
  • Introduction：唐娉，研究方向为遥感图像处理、人工智能与遥感应用。E-mail： tangping@aircas.ac.cn
TANG Ping*

résumé

The existence of a cloud reduces the application value of remote sensing images. Accurate and automatic cloud and cloud shadow detection and labeling for multispectral satellite images is conducive to the subsequent application of remote sensing images. China currently has a large number of high-resolution multispectral satellite images. However, standard data products rarely contain pixel-by-pixel cloud and cloud shadow tag data for quality analysis. Traditional cloud detection algorithms usually require parameters, such as satellite imaging geometry, imaging time, and calibration coefficients. However, many Chinese satellites’ images have lost parameter auxiliary files during multiple product iterations. Moreover, many military application satellite images are missing or do not provide parameter files. Multispectral satellite image cloud and cloud shadow detection with missing parameters requires special research.The present study investigates the cloud and cloud shadow detection method of domestic four-band multispectral satellite imagery with missing related parameters. This algorithm process is based on the classic spectral threshold cloud and cloud shadow detection algorithm. It also uses image processing and morphological algorithms to improve detection accuracy. A morphology-based method for estimating the azimuth and distance of the cloud shadow relative to the cloud area is proposed for the data with missing parameters.The experimental data in this study is from the GF-1 satellite Wide-Field View (WFV) sensor, and the 86 test images are from Dunhuang, Gansu, China. The experimental area contains a large area of a bright surface and snow-capped mountains that are easily misdetected in cloud and cloud shadow detection. The result of the cloud and cloud shadow detection experiment in this study for the case of missing parameters achieves accuracy similar to that achieved by normal algorithms. This study also analyzes the misdetection of the algorithm and clarifies the challenges of subsequent research.In this study, we propose a set of refined cloud and cloud shadow detection algorithms in case of missing parameters for domestic four-band multispectral satellite imagery. The algorithms are based on the classical spectral threshold cloud and cloud shadow detection algorithms. They also use image processing and morphological algorithms to improve accuracy further. Moreover, a morphology-based method for estimating the orientation and distance of cloud shadow relative to the cloud area is proposed for the data with missing parameters. The experimental results of GF-1 WFV data show that the detection results of this algorithm achieve an accuracy similar to that of the widely used MFC algorithm in the case of missing parameters.

mots-clés

cloud detection;cloud shadow detection;GF-1;multispectral satellite image;missing parameters

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