Improvement and Application of a GAN Model for Time Series Image Prediction-A Case Study of Time Series Satellite Cloud Images

Predicting the shape evolution and movement of remote sensing satellite cloud images is a difficult task requiring the effective monitoring and rapid prediction of thunderstorms, gales, rainstorms, and other disastrous weather conditions. We proposed a generative adversarial network (GAN) model for time series satellite cloud image prediction in this research. Taking time series information as the constraint condition and abandoning the assumption of linear and stable changes in cloud clusters in traditional methods, the GAN model is used to automatically learn the data feature distribution of satellite cloud images and predict time series cloud images in the future. Through comparative experiments and analysis, the Mish activation function is selected for integration into the model. On this basis, three improvement measures are proposed: (1) The Wasserstein distance is used to ensure the normal update of the GAN model parameters; (2) establish a multiscale network structure to improve the long-term performance of model prediction; (3) combined image gradient difference loss (GDL) to improve the sharpness of prediction cloud images. The experimental results showed that for the prediction cloud images of the next four times, compared with the unimproved Mish-GAN model, the improved GDL-GAN model improves the PSNR and SSIM by 0.44 and 0.02 on average, and decreases the MAE and RMSE by 18.84% and 7.60% on average. It is proven that the improved GDL-GAN model can maintain good visualization effects while keeping the overall changes and movement trends of the prediction cloud images relatively accurate, which is helpful to achieve more accurate weather forecast. The cooperation ability of satellite cloud images in disastrous weather forecasting and early warning is enhanced.

Automated summary

This research proposes a generative adversarial network (GAN) model for time series satellite cloud image prediction. The model learns the data feature distribution of satellite cloud images and predicts future time series cloud images by considering the time series information. Through the integration of the Mish activation function and implementation of improvement measures such as using the Wasserstein distance, establishing a multiscale network structure, and combining image gradient difference loss, the model achieves better predictive performance. The experimental results demonstrate that the improved GDL-GAN model maintains good visualization effects while accurately capturing the overall changes and movement trends of the predicted cloud images, thereby enhancing the cooperation ability of satellite cloud images in disastrous weather forecasting and early warning.