Convolutional Neural Networks Based Image Classification for Himawari-8 Stationary Satellite Imagery

Cloud plays an extremely important role in the atmosphere, which directly influences the radiation balance and indicates the potential weather and climate change. It also builds up the strength of locally thermal and dynamic processes. A novel convolutional neural networks (CNNs) approach, namely Satellites Model (SatMod), is introduced to satellite imagery classification, which performs well under multiple contrails conditions. We take contrails into account in the satellite imagery classification, which leads to satellite imagery classification more challenging than existing satellite imagery database, as clear understanding of contrails would facilitate the study of the effects of contrails on global warming. A novel dataset based on Himawari-8 stationary satellite imagery (HSSI) is proposed to represent 5 different scenes. Extensive experiments and evaluation indicate that the proposed SatMod achieves a good performance on HSSI database.