MCSIP Net: Multichannel Satellite Image Prediction via Deep Neural Network

Abstract

Satellite image prediction is important in weather nowcasting. In this article, we propose a novel multichannel satellite image prediction network (MCSIP Net) for predicting satellite images. The proposed MCSIP Net consists of three parts such as the satellite image predictor, the spatio-temporal 3-D discriminators, and the domain knowledge critic networks. The satellite image predictor takes a multichannel satellite image as an input and predicts a multichannel satellite image by learning spatio-temporal characteristics of each input channel. The spatio-temporal 3-D discriminators are trained to distinguish whether the input satellite image consists of a real satellite image or predicted image. By learning the spatio-temporal 3-D discriminator to distinguish and the satellite image predictor to deceive, the satellite image predictor can generate satellite image more similar to real satellite image distribution. The domain knowledge critic networks take the satellite image and the corresponding analysis data (which is obtained from a meteorological model) as an input and learn to distinguish whether the input satellite image is real or predicted on the basis of the analysis data. By utilizing the analysis data, the proposed MCSIP Net could take the meteorological knowledge into account efficiently. For the purpose of verification of the proposed method, ablation study and qualitative evaluation were conducted. Experimental results demonstrated that the proposed MCSIP Net could be learned efficiently and predict a multichannel satellite image with remarkable quality.