| Autor: |
Zhou Zhang, Zhenzhan Wang, Xiaolin Tong |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4716-4725 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2024.3365128 |
| Popis: |
The microwave radiation imager (MWRI) onboard the Fengyun-3D satellite can provide valuable observation data in many fields such as meteorological research and weather forecasting. However, its coarse spatial resolution limits data application. Recently, image super-resolution (SR) methods based on deep learning have been introduced into the spatial resolution enhancement of radiometers and achieved better results than traditional methods. Most of them use the degradation model to generate dataset and build model based on convolutional neural network (CNN). However, the dataset generation method based on the degradation model may impair the information in the original brightness temperature (BT) data. Moreover, CNN-based SR methods often struggle to model long-distance dependencies, which may impact the spatial resolution enhancement of BT data. To address these issues, we propose a dataset generation method based on BT data matching and introduce a SR network model based on the Transformer structure. We refer to it as the SR transformer BT data matching method. Results indicate that the method significantly improves the spatial resolution of MWRI data over current methods and exhibits strong generalization for long-term data outside the training time range. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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