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Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine

Xiaohang Shi1, Yulong Fan2, Lin Sun3

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

Scientific Reports
|November 13, 2024
PubMed
Summary
This summary is machine-generated.

Accurate nighttime cloud detection is difficult without visible data. This study uses daytime satellite images to generate nighttime samples, improving machine learning accuracy for thermal infrared cloud identification.

Keywords:
GF-5 (02)Himawari-8LightGBM (LGB)MODIS (Aqua)Nighttime cloud detectionThermal infrared (TIR)

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Area of Science:

  • Remote Sensing
  • Atmospheric Science
  • Machine Learning

Background:

  • Nighttime cloud detection in satellite imagery is challenging due to the lack of visible to near-infrared data.
  • Distinguishing clouds solely using thermal infrared bands is difficult, and obtaining sufficient nighttime training data is impractical.

Purpose of the Study:

  • To develop an effective method for nighttime cloud detection using machine learning.
  • To leverage daytime satellite imagery for generating nighttime cloud detection samples.

Main Methods:

  • Proposed a novel sample generation technique using daytime thermal infrared data for nighttime cloud detection.
  • Applied machine learning models to MODIS, GF-5 (02), and Himawari-8 satellite data.
  • Validated results using Lidar cloud products and manual labels across various surface types.

Main Results:

  • Achieved higher accuracy than existing methods (MYD35 and nighttime manual labels).
  • Specific accuracies: MODIS (82.19%), GF-5 (02) (88.71%), Himawari-8 (79.34%).
  • Performance varied across surface types, with lower accuracy over barren land, but overall high.

Conclusions:

  • The proposed daytime-to-nighttime sample generation method is effective for improving nighttime cloud detection.
  • This approach offers a novel perspective for multi-spectral satellite imagery analysis.
  • The method demonstrates improved accuracy and practicality for operational nighttime cloud detection.