Abstract
Satellite image sequence prediction is a challenging and significant task. Existing deep learning methods for the task make predictions mainly based on low-level pixel-wise features, which fail to model the sophisticated spatial-temporal features of satellite image sequences and deliver unsatisfactory performance. In this paper, 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 FY-4A satellite dataset demonstrate the superiority and effectiveness of our proposed method HSTnet over state-of-the-art approaches.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
