Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization
GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) meth...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/6/1500 |
| _version_ | 1797442665082519552 |
|---|---|
| author | Hanlin Liu Linchao Li |
| author_facet | Hanlin Liu Linchao Li |
| author_sort | Hanlin Liu |
| collection | DOAJ |
| description | GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) method that can simultaneously consider the temporal correlation of a single time series and the spatial correlation among different stations. Moreover, the method is verified using real-world regional 10-year period monitoring GNSS coordinate time series. The Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) are calculated to compare the performance of TSHMF with benchmark methods, which include the time-mean, station-mean, K-nearest neighbor, and singular value decomposition methods. The results show that the TSHMF method can reduce the MAE range from 32.03% to 12.98% and the RMSE range from 21.58% to 10.36%, proving the effectiveness of the proposed method. |
| first_indexed | 2024-03-09T12:45:15Z |
| format | Article |
| id | doaj.art-c974d86c96bc47c39c6e0ef3a6bf111e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:45:15Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c974d86c96bc47c39c6e0ef3a6bf111e2023-11-30T22:14:01ZengMDPI AGRemote Sensing2072-42922022-03-01146150010.3390/rs14061500Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix FactorizationHanlin Liu0Linchao Li1College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, ChinaGNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) method that can simultaneously consider the temporal correlation of a single time series and the spatial correlation among different stations. Moreover, the method is verified using real-world regional 10-year period monitoring GNSS coordinate time series. The Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) are calculated to compare the performance of TSHMF with benchmark methods, which include the time-mean, station-mean, K-nearest neighbor, and singular value decomposition methods. The results show that the TSHMF method can reduce the MAE range from 32.03% to 12.98% and the RMSE range from 21.58% to 10.36%, proving the effectiveness of the proposed method.https://www.mdpi.com/2072-4292/14/6/1500long-term monitoringmissing data imputationmatrix factorization |
| spellingShingle | Hanlin Liu Linchao Li Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization Remote Sensing long-term monitoring missing data imputation matrix factorization |
| title | Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization |
| title_full | Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization |
| title_fullStr | Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization |
| title_full_unstemmed | Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization |
| title_short | Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization |
| title_sort | missing data imputation in gnss monitoring time series using temporal and spatial hankel matrix factorization |
| topic | long-term monitoring missing data imputation matrix factorization |
| url | https://www.mdpi.com/2072-4292/14/6/1500 |
| work_keys_str_mv | AT hanlinliu missingdataimputationingnssmonitoringtimeseriesusingtemporalandspatialhankelmatrixfactorization AT linchaoli missingdataimputationingnssmonitoringtimeseriesusingtemporalandspatialhankelmatrixfactorization |