TEC Map Completion Using DCGAN and Poisson Blending
Abstract Because of the limited coverage of global navigation satellite system (GNSS) receivers, total electron content (TEC) maps are not complete. The processing to obtain complete TEC maps is time consuming and needs the collaboration of five international GNSS service (IGS) centers to consolidat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-05-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019SW002390 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536403883687936 |
|---|---|
| author | Yang Pan Mingwu Jin Shunrong Zhang Yue Deng |
| author_facet | Yang Pan Mingwu Jin Shunrong Zhang Yue Deng |
| author_sort | Yang Pan |
| collection | DOAJ |
| description | Abstract Because of the limited coverage of global navigation satellite system (GNSS) receivers, total electron content (TEC) maps are not complete. The processing to obtain complete TEC maps is time consuming and needs the collaboration of five international GNSS service (IGS) centers to consolidate final completed IGS TEC maps. The advance of deep learning offers powerful tools to perform certain tasks in data science, such as image completion (or inpainting). Among them, deep convolutional generative adversarial network (DCGAN) is capable of learning the properties of the objects and recovering missing data effectively. With years of IGS TEC maps for training, the combination of DCGAN and Poisson blending (DCGAN‐PB) is able to effectively learn the completion process of IGS TEC maps. Both random and more realistic masks are used to test the performance of DCGAN‐PB. The results with random masks (15–40% missing data) show that DCGAN‐PB can achieve better TEC map completion than DCGAN alone, and more training data can significantly improve its generalization. For the cross‐validation experiment using the realistic mask from Massachusetts Institute of Technology (MIT)‐TEC data (~52% missing data), DCGAN‐PB achieves the average root mean squared error about three absolute TEC units (TECu) for high solar activity years and less than two TECu for low solar activity years, which is about 50% reduction of means and more than 50% reduction on standard deviations compared to two conventional single‐image inpainting methods. The DCGAN‐PB model can lead to an efficient automatic completion tool for TEC maps by minimizing the manual work. |
| format | Article |
| id | doaj-art-59b19ac09724400193b08d83f96c7db3 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-59b19ac09724400193b08d83f96c7db32025-01-14T16:27:35ZengWileySpace Weather1542-73902020-05-01185n/an/a10.1029/2019SW002390TEC Map Completion Using DCGAN and Poisson BlendingYang Pan0Mingwu Jin1Shunrong Zhang2Yue Deng3The Department of Physics University of Texas at Arlington Arlington TX USAThe Department of Physics University of Texas at Arlington Arlington TX USAHaystack Observatory Massachusetts Institute of Technology Westford MA USAThe Department of Physics University of Texas at Arlington Arlington TX USAAbstract Because of the limited coverage of global navigation satellite system (GNSS) receivers, total electron content (TEC) maps are not complete. The processing to obtain complete TEC maps is time consuming and needs the collaboration of five international GNSS service (IGS) centers to consolidate final completed IGS TEC maps. The advance of deep learning offers powerful tools to perform certain tasks in data science, such as image completion (or inpainting). Among them, deep convolutional generative adversarial network (DCGAN) is capable of learning the properties of the objects and recovering missing data effectively. With years of IGS TEC maps for training, the combination of DCGAN and Poisson blending (DCGAN‐PB) is able to effectively learn the completion process of IGS TEC maps. Both random and more realistic masks are used to test the performance of DCGAN‐PB. The results with random masks (15–40% missing data) show that DCGAN‐PB can achieve better TEC map completion than DCGAN alone, and more training data can significantly improve its generalization. For the cross‐validation experiment using the realistic mask from Massachusetts Institute of Technology (MIT)‐TEC data (~52% missing data), DCGAN‐PB achieves the average root mean squared error about three absolute TEC units (TECu) for high solar activity years and less than two TECu for low solar activity years, which is about 50% reduction of means and more than 50% reduction on standard deviations compared to two conventional single‐image inpainting methods. The DCGAN‐PB model can lead to an efficient automatic completion tool for TEC maps by minimizing the manual work.https://doi.org/10.1029/2019SW002390TEC mapsDCGANdeep learningmap completionPoisson blendingionosphere |
| spellingShingle | Yang Pan Mingwu Jin Shunrong Zhang Yue Deng TEC Map Completion Using DCGAN and Poisson Blending Space Weather TEC maps DCGAN deep learning map completion Poisson blending ionosphere |
| title | TEC Map Completion Using DCGAN and Poisson Blending |
| title_full | TEC Map Completion Using DCGAN and Poisson Blending |
| title_fullStr | TEC Map Completion Using DCGAN and Poisson Blending |
| title_full_unstemmed | TEC Map Completion Using DCGAN and Poisson Blending |
| title_short | TEC Map Completion Using DCGAN and Poisson Blending |
| title_sort | tec map completion using dcgan and poisson blending |
| topic | TEC maps DCGAN deep learning map completion Poisson blending ionosphere |
| url | https://doi.org/10.1029/2019SW002390 |
| work_keys_str_mv | AT yangpan tecmapcompletionusingdcganandpoissonblending AT mingwujin tecmapcompletionusingdcganandpoissonblending AT shunrongzhang tecmapcompletionusingdcganandpoissonblending AT yuedeng tecmapcompletionusingdcganandpoissonblending |