An Effective Convolutional Neural Network for Visualized Understanding Transboundary Air Pollution Based on Himawari-8 Satellite Images

Air pollution is a societal and cross-boundary environmental problem that can be visualized using a satellite. Satellite imaging is not only useful to the home country but also to the neighboring countries. Moreover, monitoring the movement of air pollution can help susceptible people avoid acid rain and photochemical smog. Using advanced remote sensing (RS) images, substantial information can be obtained, which can produce numerous effective methods for visualizing air pollution. In this article, a novel method for extracting air pollution has been proposed; it applies various pipeline networks along with a focus area method to exploit the spectral aspect information. Afterward, three indices with numerous modified fully convolutional networks (FCNs) were extracted. Then, by employing a multivote module, visualized air pollution can be presented. In the conducted experiments, five-year Himawari-8 satellite images have been utilized in the North-East Asia area to validate the frameworks. Furthermore, the experimental result indicating that the given methods could effectively visualize air pollution. Source code and data sets are available at https://github.com/ark1234/Himawari-8-based-visualized-understanding.