Multi-source deep data fusion and super-resolution for downscaling sea surface temperature guided by Generative Adversarial Network-based spatiotemporal dependency learning
In this study, we propose a deep learning framework for multi-source deep data fusion and super-resolution for generative adversarial network-based spatiotemporal dependency learning to produce accurately downscaled sea surface temperature (SST) through simultaneously achieving error correction and...
Main Authors: | Jinah Kim, Taekyung Kim, Joon-Gyu Ryu |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-05-01
|
Series: | International Journal of Applied Earth Observations and Geoinformation |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223001346 |
Similar Items
-
A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme
by: Jia Liu, et al.
Published: (2022-02-01) -
Remote Sensing Image Super-Resolution Adversarial Network Based on Reverse Feature Fusion and Residual Feature Dilation
by: Rui Han, et al.
Published: (2023-01-01) -
ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India
by: Sumanta Chandra Mishra Sharma, et al.
Published: (2022-01-01) -
Spatial Transformer Generative Adversarial Network for Robust Image Super-Resolution
by: Hossam M. Kasem, et al.
Published: (2019-01-01) -
A precipitation downscaling method using a super-resolution deconvolution neural network with step orography
by: P. Jyoteeshkumar Reddy, et al.
Published: (2023-01-01)