Exploiting Spatial–Temporal Dynamics for Satellite Image Sequence Prediction

Satellite image sequence prediction is a challenging and significant task. The existing deep learning methods for the task make predictions mainly based on low-level pixelwise features, which fail to model the sophisticated spatial–temporal features of satellite image sequences and deliver unsatisfactory performance. In this letter, we present a hierarchical spatial–temporal network (HSTnet) for satellite image sequence prediction. With a carefully designed hierarchical feature extraction mechanism, HSTnet can learn effective spatial–temporal features from both pixel level and patch level. In addition, to better capture patch-level spatial–temporal dynamics, a dual-branch Transformer is proposed to model patch-level spatial and temporal features, respectively. Comprehensive experiments on the Fengyun-4A (FY-4A) satellite dataset demonstrate the superiority and effectiveness of our proposed method HSTnet over state-of-the-art approaches.