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Exploiting Spatial–Temporal Dynamics for Satellite Image Sequence Prediction.
- Source :
- IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p
- Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1545598X
- Volume :
- 20
- Database :
- Complementary Index
- Journal :
- IEEE Geoscience & Remote Sensing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 176253251
- Full Text :
- https://doi.org/10.1109/LGRS.2023.3261317