.png){kind=icon}
返回
Learning Spatial-Temporal Consistency for Satellite Image Sequence Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3303947.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As an extremely challenging spatial-temporal sequence prediction task, satellite image sequence prediction has various and significant applications in real-world scenarios. Although a lot of deep learning prediction models are developed for spatial-temporal sequence prediction, the methods still deliver unsatisfactory performance in terms of keeping spatial-temporal consistency, which leads to inaccurate and blurry satellite image sequence predictions. To maintain spatial-temporal consistency and achieve high-quality satellite image sequence prediction, we propose a novel and effective spatial-temporal consistency network (STCNet). In STCNet, a multilevel motion memory-based predictor is proposed to accurately predict motion patterns of satellite image sequences to ensure temporal consistency. Then, a time-variant frame discriminator is carefully designed and proposed, which can enhance the perception quality of predicted frames to guarantee spatial consistency and simultaneously maintain the motion coherency of predicted sequences. Moreover, a scheduled sampling strategy is proposed to reduce the optimizing difficulty and better train the proposed method. Comprehensive experiments conducted on satellite image sequences from the FY-4A meteorological satellite verify the effectiveness, applicability, and adaptability of our proposed method compared to state-of-the-art approaches under challenging scenarios.
Keyword:
Deep learning
satellite image sequence pre-diction
spatial-temporal consistency
spatial-temporal sequence prediction
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Judgment Capacity, Fear of Falling, and the Risk of Falls in Community-Dwelling Older Adults: The Progetto Veneto Anziani Longitudinal Study社区居住的老年人的判断能力，对跌倒的恐惧和跌倒的风险: Progetto Veneto Anziani纵向研究
OAAI
OAAI分享收藏Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning基于机器学习的Himawari-8和全球预报系统估算夏季降水
PREAI
分享收藏Mechanisms and Dynamics of Layered Structure Formation During Co-Deposition of Binary Compound Thin Films.png)
Coatings
OAAI
CoatingsIF0
OAAI分享收藏分享收藏
查看更多内容