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SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction.
- Source :
- Journal of Electronic Imaging; Sep/Oct2022, Vol. 31 Issue 5, p53020-53020-16, 1p
- Publication Year :
- 2022
-
Abstract
- To solve the problems of poor authenticity and lack of clarity for short-time typhoon prediction, we propose a self-attentional spatiotemporal adversarial network (SASTA-Net). First, we introduce a multispatiotemporal feature fusion method to fully extract and fuse the multichannel spatiotemporal feature information to effectively enhance feature expression. Second, we propose an SATA-LSTM prediction model that incorporates spatial memory cell and attention mechanisms in order to capture spatial features and important details in sequences. Finally, a spatiotemporal 3D discriminator is designed to correctly distinguish the generated predicted cloud image from the real cloud image and generate a more accurate and real typhoon cloud image by adversarial training. The evaluation results on the typhoon cloud image data set show that the proposed SASTA-Net achieves 67.3, 0.878, 31.27, and 56.48 in mean square error, structural similarity, peak signal to noise ratio, and sharpness, respectively, which is superior to the most advanced prediction algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10179909
- Volume :
- 31
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Electronic Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 159958518
- Full Text :
- https://doi.org/10.1117/1.JEI.31.5.053020