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Exploiting Spatial-Temporal Dynamics for Satellite Image Sequence Prediction
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AI
DOI：10.1109/LGRS.2023.3261317.png)
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Abstract
En 中文
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.
Keywords:
Satellites
Image sequences
Feature extraction
Transformers
Task analysis
Predictive models
Dynamics
Patch-level features
pixelwise features
satellite image sequence prediction
spatial-temporal features
Journal
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