Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning

Retrieving atmospheric environmental parameters such as horizontal visibility and mass concentration of aerosol particles with a diameter 2.5 or 10 ${\mu }\text{m}$ less (PM 2.5 , PM xmlns:xlink="http://www.w3.org/1999/xlink">10 respectively) from digital images provides new tools for monitoring. In this study, we propose end-to-end convolutional neural network (CNN) the retrieval multiple (RMEPs) images. contrast to other models, RMEP can retrieve suite including visibility, relative humidity (RH), ambient temperature, simultaneously single image. Experimental results demonstrate that: 1) it is possible parameters; 2) spatial spectral resolutions are not key factors on scale; 3) achieves best overall performance compared several classic CNNs AlexNet, ResNet-50, DenseNet-121, based experiments extracted webcams located in different continents (test notation="LaTeX">$R^{2}$ values 0.63, 0.72, 0.82 RH, respectively). show potential utilizing help monitor environment. Code more available at https://github.com/cvvsu/RMEP .