Retrieving atmospheric environmental parameters such as atmospheric horizontal visibility and mass concentration of aerosol particles with a diameter of 2.5 or 10 μm or less (PM 2.5 , PM 10 , respectively) from digital images provides new tools for horizontal environmental monitoring. In this study, we propose a new end-to-end convolutional neural network (CNN) for the retrieval of multiple atmospheric environmental parameters (RMEPs) from images. In contrast to other retrieval models, RMEP can retrieve a suite of atmospheric environmental parameters including atmospheric horizontal visibility, relative humidity (RH), ambient temperature, PM 2.5 , and PM 10 simultaneously from a single image. Experimental results demonstrate that: 1) it is possible to simultaneously retrieve multiple atmospheric environmental parameters; 2) spatial and spectral resolutions of images are not the key factors for the retrieval on the horizontal scale; and 3) RMEP achieves the best overall retrieval performance compared with several classic CNNs such as AlexNet, ResNet-50, and DenseNet-121, and the results are based on experiments on images extracted from webcams located in different continents (test R2 values are 0.63, 0.72, and 0.82 for atmospheric horizontal visibility, RH, and ambient temperature, respectively). Experimental results show the potential of utilizing webcams to help monitor the environment. Code and more results are available at https://github.com/cvvsu/RMEP .
Original languageEnglish
Article number1005005
JournalIEEE Geoscience and Remote Sensing Letters
Number of pages5
Publication statusPublished - 4 Feb 2022
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1171 Geosciences
  • 113 Computer and information sciences
  • Atmospheric modeling
  • Feature extraction
  • Convolutional neural networks
  • Atmospheric measurements
  • Spatial resolution
  • Aerosols
  • Webcams
  • Atmospheric pollutant
  • convolutional neural network (CNN)
  • deep learning
  • environmental monitoring
  • image processing
  • meteorological parameters

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