research-article

TinyPredNet: A Lightweight Framework for Satellite Image Sequence Prediction

Authors:

Di XianAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 5

Article No.: 142, Pages 1 - 24

https://doi.org/10.1145/3638773

Published: 22 January 2024 Publication History

Abstract

Satellite image sequence prediction aims to precisely infer future satellite image frames with historical observations, which is a significant and challenging dense prediction task. Though existing deep learning models deliver promising performance for satellite image sequence prediction, the methods suffer from quite expensive training costs, especially in training time and GPU memory demand, due to the inefficiently modeling for temporal variations. This issue seriously limits the lightweight application in satellites such as space-borne forecast models. In this article, we propose a lightweight prediction framework TinyPredNet for satellite image sequence prediction, in which a spatial encoder and decoder model the intra-frame appearance features and a temporal translator captures inter-frame motion patterns. To efficiently model the temporal evolution of satellite image sequences, we carefully design a multi-scale temporal-cascaded structure and a channel attention-gated structure in the temporal translator. Comprehensive experiments are conducted on FengYun-4A (FY-4A) satellite dataset, which show that the proposed framework achieves very competitive performance with much lower computation cost compared to state-of-the-art methods. In addition, corresponding interpretability experiments are conducted to show how our designed structures work. We believe the proposed method can serve as a solid lightweight baseline for satellite image sequence prediction.

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Cited By

Xie BDong JLiu CCheng W(2024)MEHGNet: a multi-feature extraction and high-resolution generative network for satellite cloud image sequence predictionEarth Science Informatics10.1007/s12145-024-01432-117:5(4931-4948)Online publication date: 6-Aug-2024
https://doi.org/10.1007/s12145-024-01432-1

Index Terms

TinyPredNet: A Lightweight Framework for Satellite Image Sequence Prediction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture
    2. Knowledge representation and reasoning
      1. Temporal reasoning
2. Theory of computation
  1. Theory and algorithms for application domains

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 5

May 2024

650 pages

EISSN:1551-6865

DOI:10.1145/3613634

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 22 January 2024

Online AM: 28 December 2023

Accepted: 23 December 2023

Revised: 27 September 2023

Received: 16 July 2023

Published in TOMM Volume 20, Issue 5

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Shenzhen Science and Technology Program
FengYun Application Pioneering Project
Science and Technology Innovation Team Project of Guangdong Meteorological Bureau
Innovation and Development Project of China Meteorological Administration
NSFC

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Cited By

Xie BDong JLiu CCheng W(2024)MEHGNet: a multi-feature extraction and high-resolution generative network for satellite cloud image sequence predictionEarth Science Informatics10.1007/s12145-024-01432-117:5(4931-4948)Online publication date: 6-Aug-2024
https://doi.org/10.1007/s12145-024-01432-1

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