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Deep Convolutional Neural Network for Fog Detection

  • Jun ZhangEmail author
  • Hui Lu
  • Yi Xia
  • Ting-Ting Han
  • Kai-Chao Miao
  • Ye-Qing Yao
  • Cheng-Xiao Liu
  • Jian-Ping Zhou
  • Peng Chen
  • Bing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Fog detection has becomes more and more important in recent years, real-time monitoring information is very beneficial for people to arrange production and life. In this paper, based on meterological satellite data (Himawari-8 standard data, HSD8), Covolutional Neural Network (CNN) is used to detect fog. Since HSD8 consists of 16 channels, the original CNN is extended to multiple channels for HSD8. Multiple Channels CNN (MCCNN) can make the full exploitation of spatial and spectral information effectively. A dataset is created from Anhui Area which consists of ground station data and grid data. Different image sizes and convolutional kernels are used to validate the proposed methods. The experimental results show that the proposed method achieves 91.87% accuracy.

Keywords

Fog CNN Detection Himawari-8 

Notes

Acknowledgments

This work was supported by Jiangsu Province Meteorological Bureau Bei Ji Ge grant Nos. BJG201707, Anhui Province Meteorological Bureau meteorologist special grant Nos. KY201704, Anhui Provincial Natural Science Foundation (grant number 1608085MF136).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhang
    • 1
    Email author
  • Hui Lu
    • 1
  • Yi Xia
    • 1
  • Ting-Ting Han
    • 1
  • Kai-Chao Miao
    • 2
  • Ye-Qing Yao
    • 2
  • Cheng-Xiao Liu
    • 2
  • Jian-Ping Zhou
    • 2
  • Peng Chen
    • 3
  • Bing Wang
    • 4
  1. 1.School of Electronic Engineering and AutomationAnhui UniversityHefeiChina
  2. 2.Anhui Meteorological BureauHefeiChina
  3. 3.Institute of Health SciencesAnhui UniversityHefeiChina
  4. 4.School of Electrical and Information EngineeringAnhui University of TechnologyMa AnshanChina

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