Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks
Visible (VIS) imagery is important for monitoring tropical cyclones (TCs) but is unavailable at night. This study presents a conditional generative adversarial networks model to generate nighttime VIS imagery with significantly enhanced accuracy and spatial resolution. Our method offers three key im...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10988561/ |
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| author | Jinghuai Yao Puyuan Du Yucheng Zhao Yubo Wang |
| author_facet | Jinghuai Yao Puyuan Du Yucheng Zhao Yubo Wang |
| author_sort | Jinghuai Yao |
| collection | DOAJ |
| description | Visible (VIS) imagery is important for monitoring tropical cyclones (TCs) but is unavailable at night. This study presents a conditional generative adversarial networks model to generate nighttime VIS imagery with significantly enhanced accuracy and spatial resolution. Our method offers three key improvements compared to existing models. First, we replaced the L1 loss in the pix2pix framework with the structural similarity index measure (SSIM) loss, which significantly reduced image blurriness. Second, we selected multispectral infrared bands as input based on a thorough examination of their spectral properties, providing essential physical information for accurate simulation. Third, we incorporated the direction parameters of the sun and the satellite, which addressed the dependence of VIS images on sunlight directions and enabled a much larger training set from continuous daytime data. The model was trained and validated using data from the advanced Himawari imager in the daytime, achieving statistical results of SSIM = 0.923 and root mean square error = 0.0299, which significantly surpasses existing models. We also performed a cross-satellite nighttime model validation using the day/night band of the visible/infrared imager radiometer suite, which yields outstanding results compared to existing models. Our model is operationally applied to generate accurate VIS imagery with arbitrary virtual sunlight directions, significantly contributing to the nighttime monitoring of various meteorological phenomena. |
| format | Article |
| id | doaj-art-4c0f5a72adbf4dfd9a55dcacb8aa831d |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-4c0f5a72adbf4dfd9a55dcacb8aa831d2025-08-20T01:53:04ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118126161263310.1109/JSTARS.2025.356707410988561Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial NetworksJinghuai Yao0https://orcid.org/0009-0002-0525-8222Puyuan Du1https://orcid.org/0009-0003-9088-9586Yucheng Zhao2https://orcid.org/0009-0006-5259-0868Yubo Wang3https://orcid.org/0009-0004-7125-9762Department of Astronomy, University of Wisconsin–Madison, Madison, WI, USADepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USASchool of Health Sciences, Guangzhou Xinhua University, Guangzhou, ChinaDepartment of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI, USAVisible (VIS) imagery is important for monitoring tropical cyclones (TCs) but is unavailable at night. This study presents a conditional generative adversarial networks model to generate nighttime VIS imagery with significantly enhanced accuracy and spatial resolution. Our method offers three key improvements compared to existing models. First, we replaced the L1 loss in the pix2pix framework with the structural similarity index measure (SSIM) loss, which significantly reduced image blurriness. Second, we selected multispectral infrared bands as input based on a thorough examination of their spectral properties, providing essential physical information for accurate simulation. Third, we incorporated the direction parameters of the sun and the satellite, which addressed the dependence of VIS images on sunlight directions and enabled a much larger training set from continuous daytime data. The model was trained and validated using data from the advanced Himawari imager in the daytime, achieving statistical results of SSIM = 0.923 and root mean square error = 0.0299, which significantly surpasses existing models. We also performed a cross-satellite nighttime model validation using the day/night band of the visible/infrared imager radiometer suite, which yields outstanding results compared to existing models. Our model is operationally applied to generate accurate VIS imagery with arbitrary virtual sunlight directions, significantly contributing to the nighttime monitoring of various meteorological phenomena.https://ieeexplore.ieee.org/document/10988561/Advanced Himawari imager (AHI)cloudsconditional generative adversarial network (CGAN)deep learningnighttimetropical cyclone (TC) |
| spellingShingle | Jinghuai Yao Puyuan Du Yucheng Zhao Yubo Wang Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Advanced Himawari imager (AHI) clouds conditional generative adversarial network (CGAN) deep learning nighttime tropical cyclone (TC) |
| title | Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks |
| title_full | Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks |
| title_fullStr | Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks |
| title_full_unstemmed | Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks |
| title_short | Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks |
| title_sort | simulating nighttime visible satellite imagery of tropical cyclones using conditional generative adversarial networks |
| topic | Advanced Himawari imager (AHI) clouds conditional generative adversarial network (CGAN) deep learning nighttime tropical cyclone (TC) |
| url | https://ieeexplore.ieee.org/document/10988561/ |
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