A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting
Due to the uneven distribution of ground observatories, the effective data coverage of global ionospheric TEC is below 50%. The International GNSS Service provides a global ionosphere map based on a single shell assumption, derived from the ground-based observations. This serves as the ma...
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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/11087524/ |
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| author | Kunlin Yang Yang Liu Yifei Chen Zhizhao Liu Kaiyan Jin Yanbo Zhu |
| author_facet | Kunlin Yang Yang Liu Yifei Chen Zhizhao Liu Kaiyan Jin Yanbo Zhu |
| author_sort | Kunlin Yang |
| collection | DOAJ |
| description | Due to the uneven distribution of ground observatories, the effective data coverage of global ionospheric TEC is below 50%. The International GNSS Service provides a global ionosphere map based on a single shell assumption, derived from the ground-based observations. This serves as the main reference for global ionosphere morphology study. In this work, a new GAN model, wavelet transform and merging generative adversarial network (WTM-GAN) is proposed, designed for spatial completion of ionospheric TEC data with observation coverage deficiency. WTM-GAN is designed with an encoder–decoder architecture, using a Haar wavelet filter and a multilayer decoder employing segmentation and merging techniques. The performance is rigorously tested, achieving root-mean-square errors of 2.117 TECu and 0.908 TECu during both high and low solar activity years, respectively, and it obtains improvement of 0.945 TECu and 0.739 TECu over the comparison models. It also attained a peak signal-to-noise ratio over 32 dB, outperforming all comparisons. During geomagnetic storms, WTM-GAN effectively captures features in the equatorial ionization anomaly region, demonstrating enhanced spatial observation augmentation accuracy and stability. This framework offers a robust solution for TEC data completion, improving the reliability of ionospheric studies. |
| format | Article |
| id | doaj-art-c9c7a4199fc044779a67d29f7765e8bb |
| institution | Kabale University |
| 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-c9c7a4199fc044779a67d29f7765e8bb2025-08-22T23:05:47ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118205302054410.1109/JSTARS.2025.359110311087524A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial InpaintingKunlin Yang0https://orcid.org/0009-0008-8310-8282Yang Liu1https://orcid.org/0000-0003-1793-0645Yifei Chen2Zhizhao Liu3Kaiyan Jin4https://orcid.org/0009-0006-0785-0958Yanbo Zhu5https://orcid.org/0000-0003-4579-795XSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, P.R. ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, P.R. ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, P.R. ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P.R. ChinaSchool of Electronic Information Engineering, Beihang University, Beijing, P.R. ChinaSchool of Electronic Information Engineering, Beihang University, Beijing, P.R. ChinaDue to the uneven distribution of ground observatories, the effective data coverage of global ionospheric TEC is below 50%. The International GNSS Service provides a global ionosphere map based on a single shell assumption, derived from the ground-based observations. This serves as the main reference for global ionosphere morphology study. In this work, a new GAN model, wavelet transform and merging generative adversarial network (WTM-GAN) is proposed, designed for spatial completion of ionospheric TEC data with observation coverage deficiency. WTM-GAN is designed with an encoder–decoder architecture, using a Haar wavelet filter and a multilayer decoder employing segmentation and merging techniques. The performance is rigorously tested, achieving root-mean-square errors of 2.117 TECu and 0.908 TECu during both high and low solar activity years, respectively, and it obtains improvement of 0.945 TECu and 0.739 TECu over the comparison models. It also attained a peak signal-to-noise ratio over 32 dB, outperforming all comparisons. During geomagnetic storms, WTM-GAN effectively captures features in the equatorial ionization anomaly region, demonstrating enhanced spatial observation augmentation accuracy and stability. This framework offers a robust solution for TEC data completion, improving the reliability of ionospheric studies.https://ieeexplore.ieee.org/document/11087524/Generative adversarial network (GAN)ionospherespatial inpaintingtotal electron content (TEC) |
| spellingShingle | Kunlin Yang Yang Liu Yifei Chen Zhizhao Liu Kaiyan Jin Yanbo Zhu A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Generative adversarial network (GAN) ionosphere spatial inpainting total electron content (TEC) |
| title | A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting |
| title_full | A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting |
| title_fullStr | A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting |
| title_full_unstemmed | A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting |
| title_short | A New Wavelet Transform and Merging Generative Adversarial Network (WTM-GAN) Model for TEC Spatial Inpainting |
| title_sort | new wavelet transform and merging generative adversarial network wtm gan model for tec spatial inpainting |
| topic | Generative adversarial network (GAN) ionosphere spatial inpainting total electron content (TEC) |
| url | https://ieeexplore.ieee.org/document/11087524/ |
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