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Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12305))

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Abstract

Cloud detection plays a very important role in the development of satellite remote sensing products and influences the accuracy of satellite products that characterize the properties of clouds, aerosols, trace gases, and ground surface parameters. However, current existing cloud detection methods rely heavily on the data of visible bands. It makes FY3D MERSI, which lacks visible band data at night, difficult to use these methods with high accuracy. In this paper, we proposed a cloud detection method based on deep learning termed CM-CNN for FY-3D MERSI. In order to ensure the effect of the network, the data has been strictly selected and consequently preprocessed. The method can automatically extract identified target features and fuse multi-level feature information, and adjust the parameters in the network without setting a threshold. Besides, this method proves to be better and more robust while only using mid-infrared and long-infrared band data in different cases.

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Acknowledgments

This work is supported by the National Key Research and Development Plan of China (Grant No. 2018YFB0504901) and the National Natural Science foundation of China (Grant No. 41871249). The authors also acknowledge the National Satellite Meteorological Center for the imagery of FY-3D MERSI data.

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Authors and Affiliations

  1. Key Laboratory of Photoelectronic Imaging Technology, Ministry of Education of China, School of Optoelectronics, Beijing Insitute of Technology, Beijing, 100081, China

    Yutong Ding, Yuqing He, Mingqi Liu & Saijie Wang

  2. National Satellite Meteorological Center, Beijing, 100081, China

    Xiuqing Hu

Authors
  1. Yutong Ding
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  2. Xiuqing Hu
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  3. Yuqing He
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  4. Mingqi Liu
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  5. Saijie Wang
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Corresponding author

Correspondence to Yuqing He .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Ding, Y., Hu, X., He, Y., Liu, M., Wang, S. (2020). Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_51

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  • DOI: https://doi.org/10.1007/978-3-030-60633-6_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60632-9

  • Online ISBN: 978-3-030-60633-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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