Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method
The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on elimi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/14/3695 |
| _version_ | 1797587569103339520 |
|---|---|
| author | Xingdong Shen Cui Zhou Jianjun Zhu |
| author_facet | Xingdong Shen Cui Zhou Jianjun Zhu |
| author_sort | Xingdong Shen |
| collection | DOAJ |
| description | The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy of TanDEM-X DEM. The experimental results demonstrate that TanDEM-X DEM corrected by the LSC-TXC algorithm reduces the root mean square error (RMSE) from 6.141 m to 3.851 m, resulting in a significant improvement in accuracy (by 37.3%). Compared to three conventional algorithms, namely Random Forest, Height Difference Fitting Neural Network and Back Propagation in Neural Network, the presented algorithm demonstrates a reduction in the RMSEs of the corrected TanDEM-X DEMs by 6.5%, 7.6%, and 18.1%, respectively. This algorithm provides an efficient tool for correcting DEMs such as TanDEM-X for a wide range of areas. |
| first_indexed | 2024-03-11T00:40:45Z |
| format | Article |
| id | doaj.art-1ee70b42ef72483fb4e16cdb4ecbbd8f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:40:45Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1ee70b42ef72483fb4e16cdb4ecbbd8f2023-11-18T21:14:23ZengMDPI AGRemote Sensing2072-42922023-07-011514369510.3390/rs15143695Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation MethodXingdong Shen0Cui Zhou1Jianjun Zhu2College of Computer and Information Engineering & College of Science, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Computer and Information Engineering & College of Science, Central South University of Forestry and Technology, Changsha 410004, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaThe TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy of TanDEM-X DEM. The experimental results demonstrate that TanDEM-X DEM corrected by the LSC-TXC algorithm reduces the root mean square error (RMSE) from 6.141 m to 3.851 m, resulting in a significant improvement in accuracy (by 37.3%). Compared to three conventional algorithms, namely Random Forest, Height Difference Fitting Neural Network and Back Propagation in Neural Network, the presented algorithm demonstrates a reduction in the RMSEs of the corrected TanDEM-X DEMs by 6.5%, 7.6%, and 18.1%, respectively. This algorithm provides an efficient tool for correcting DEMs such as TanDEM-X for a wide range of areas.https://www.mdpi.com/2072-4292/15/14/3695least squares collocation methodsystematic errorrandom errorTanDEM-X DEMICESat-2 |
| spellingShingle | Xingdong Shen Cui Zhou Jianjun Zhu Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method Remote Sensing least squares collocation method systematic error random error TanDEM-X DEM ICESat-2 |
| title | Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method |
| title_full | Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method |
| title_fullStr | Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method |
| title_full_unstemmed | Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method |
| title_short | Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method |
| title_sort | improving the accuracy of tandem x digital elevation model using least squares collocation method |
| topic | least squares collocation method systematic error random error TanDEM-X DEM ICESat-2 |
| url | https://www.mdpi.com/2072-4292/15/14/3695 |
| work_keys_str_mv | AT xingdongshen improvingtheaccuracyoftandemxdigitalelevationmodelusingleastsquarescollocationmethod AT cuizhou improvingtheaccuracyoftandemxdigitalelevationmodelusingleastsquarescollocationmethod AT jianjunzhu improvingtheaccuracyoftandemxdigitalelevationmodelusingleastsquarescollocationmethod |