Remote Sensing Cloud Image Prediction Method Based on Multi-scale Motion Memory Model

Existing deep learning models find it difficult to capture cloud motion patterns, resulting in long-term cloud prediction results that are fuzzy and low in accuracy. To address this problem, this study proposes a remote sensing cloud image prediction method based on a Multi-Scale Motion Memory Network (MSMM_Net). This model adopts a dual-branch memory-flow architecture that combines spatial multi-scale and motion-differential memory flows. It extracts high- and low-frequency spatial features and sequence motion features hidden in the input image sequence, thereby simultaneously obtaining global, detail, and motion information of the image. In the prediction stage, dual-branch memory is fused to alleviate the problem of feature loss and enhance the ability of the model to predict the trajectory of cloud clusters. On this basis, a fusion loss function combining pixel and edge losses is used to guide model training, enhance the model's attention to image edge details, and promote the generation of clear predicted images. Experimental results show that, compared with the benchmark model PredRNN, MSMM_Net reduces the Mean Square Error (MSE) by 31.71% on the Moving MNIST dataset and the Learned Perceptual Image Patch Similarity (LPIPS) by 64.7%. On the remote sensing satellite cloud image dataset, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) indicators improve by 5.51% and 5.38%, respectively, indicating that the predicted image sequence generated by the model is more similar to the real image sequence and can effectively improve long-term prediction accuracy.