Time-Series FY4A Datasets for Super-Resolution Benchmarking of Meteorological Satellite Images

Meteorological satellites are usually operated at high temporal resolutions, but the spatial resolutions are too poor to identify ground content. Super-resolution is an economic way to enhance spatial details, but the feasibility is not validated for meteorological images due to the absence of bench...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 21; p. 5594
Main Authors Wei, Jingbo, Zhou, Chenghao, Wang, Jingsong, Chen, Zhou
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
Algorithms
Benchmarks
dataset
Datasets
Deep learning
Fengyun-4A
FY4A
Image enhancement
Machine learning
Meteorological satellites
Parameter modification
Performance evaluation
Performance tests
Rain
Remote sensing
Satellite imagery
Sensors
super resolution
China
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Summary:Meteorological satellites are usually operated at high temporal resolutions, but the spatial resolutions are too poor to identify ground content. Super-resolution is an economic way to enhance spatial details, but the feasibility is not validated for meteorological images due to the absence of benchmarking data. In this work, we propose the FY4ASRgray and FY4ASRcolor datasets to assess super-resolution algorithms on meteorological applications. The features of cloud sensitivity and temporal continuity are linked to the proposed datasets. To test the usability of the new datasets, five state-of-the-art super-resolution algorithms are gathered for contest. Shift learning is used to shorten the training time and improve the parameters. Methods are modified to deal with the 16-bit challenge. The reconstruction results are demonstrated and evaluated regarding the radiometric, structural, and spectral loss, which gives the baseline performance for detail enhancement of the FY4A satellite images. Additional experiments are made on FY4ASRcolor for sequence super-resolution, spatiotemporal fusion, and generalization test for further performance test.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14215594