Document Type : Research Paper


1 Department of surveying engineering, Arak University of Technology, Arak, Iran

2 Department of Surveying Engineering, Arak University of Technology, Arak, Iran



Extended Abstract
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabörk, 2016).
Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018).
Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages,which were investigated in previous studies (Capaldo et al., 2015).
With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation.
Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagüe-Martínez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2.Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012).
The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.
Wavelet transform and 2D wavelet transform methods
As a spectral analysis tool, wavelet transform is based on expanding any function like f(t)



inwhichaiis the expansion coefficient and 𝜓iis the expansion function.
One of the interesting characteristics of discrete wavelet transform is that it can be used as a multi-resolution analysis tool. To do so, a series of scaling functions or are used along with the wavelets to determine coarse data of the signals. Signal detailsare also covered by different wavelets with different scales.
Separatingcoarsedataand details of the signal isthe actual basis of discrete wavelet transform algorithm which wasintroduced by Mallat (Mallat, 1989) and improved by Beylkin et al. (Beylkin, Coifman, &Rokhlin, 1991). As a fast and simple method for discrete wavelet transform,the process is performed based on the followingrecursive relationships betweenahighpass and a lowpass filters with the impulse responses h(n) and g(n), respectively (Primer et al., 1998):






Where the expansion coefficients h and g are scaling filter and wavelet respectively.






These formulas can be expanded to calculate 2D discrete wavelet transform.
Proposed Method
This section proposes a method of enhancing DEM generated from Sentinel-1 imagery using SRTM DEM and 2D wavelet transform. Considering the capability of wavelet transform as a multi-resolution analysis tool which can separate coarse data from details, figure 2 shows the proposed process of improving DEM. First, using discrete 2DWT, coarse information and details of each DEMs are separated using the ascending and descending conditions of Sentinel-1 images. Then, two stages are considered based on the nature of these models. First, filtering coefficients usingthresholding and considering the average as the detail or high frequency part of the enhanced model. Second, coarse information derived from wavelet transform method have a resolution of40m and thus data derived from SRTM (30m) has a higher quality. Therefore, inverse 2DWT will improve the results and reach a resolution of 20 m. 
Experiments and Results
The study area is located in Northern areas of Tehran (Iran) at the UTM coordinates of (542450 ,3964590) northeast to (539010, 3962350) southwest. Two Sentinel-1 satellite image pairs (one ascending and one descending) are used in this study.
In addition, a SRTM DEM with a spatial resolution of30m is used to improve DEM generated from Sentinel-1 images. Sentinel-1 derived DEM is evaluated using the 1m resolutionreference DEM. RMSE values shows the effectiveness of the proposed method in enhancing the Sentinel-1 derived DEM, which means that using information obtained from the SRTM and 2D wavelet transform was reasonable. RMSE values are reduced from 24.2097m to 11.1749m which shows 54% improvement. The proposed method can enhance results to 30 - 82 percentapproximately.
The present study investigates methods used for generating DEM from satellite images especially Sentinel-1 radar imagery. DEM derived from Sentinel-1 data has a high spatial resolution.Yet, it has some outliers or errors in elevation points whichneeds to be modified.  Therefore, the present study proposes a method based on 2D wavelet transformfor deriving elevation model witha spatial resolution (20m) equal to that of Sentinel-1 DEM and improved precision and accuracy. In this method, filtering the details of the model using discrete 2D wavelet transform and modifying coarse information using SRTM DEM results in an enhanced DEM with higher spatial resolution.


1.   Balz, T., Zhang, L., & Liao, M. (2013). Direct stereo radargrammetric processing using massively parallel processing. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 137-146.
2.   Beylkin, G., Coifman, R., &Rokhlin, V. (1991). Fast wavelet transforms and numerical algorithms I. Communications on pure and applied mathematics, 44(2), 141-183.
3.   Capaldo, P., Nascetti, A., Porfiri, M., Pieralice, F., Fratarcangeli, F., Crespi, M., &Toutin, T. (2015). Evaluation and comparison of different radargrammetric approaches for Digital Surface Models generation from COSMO-SkyMed, TerraSAR-X, RADARSAT-2 imagery: Analysis of Beauport (Canada) test site. ISPRS Journal of Photogrammetry and Remote Sensing, 100, 60-70.
4.   Chu, T., Das, A., &Lindenschmidt, K.-E. (2017). RADARSAT-2-based digital elevation models derived from InSAR for high latitudes of northern Canada. Journal of Applied Remote Sensing, 11(3), 035013.
5.   Clement, M., Kilsby, C., & Moore, P. (2018). Multi‐temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152-168.
6.   Dong, Y., Zhang, L., Balz, T., Luo, H., & Liao, M. (2018). Radargrammetric DSM generation in mountainous areas through adaptive-window least squares matching constrained by enhanced epipolar geometry. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 61-72.
7.   Enayati, H., Khesali, E. (2019). Generating Digital Elevation Model of the Earth Using Sentinel-1 Images and Interferometry. GEOGRAPHICAL DATA, 109-121.
8.   Gao, X., Liu, Y., Li, T., & Wu, D. (2017). High precision DEM generation algorithm based on InSAR multi-look iteration.Remote Sensing, 9(7), 741.
9.   Geymen, A. (2014). Digital elevation model (DEM) generation using the SAR interferometry technique.Arabian Journal of Geosciences, 7(2), 827-83.
10. Ghannadi, M., &SaadatSeresht, M. (2014). A Multi-Step Strategy for SAR Image Matching. Journal of Geomatics Science and Technology, 4(2), 11-24.
11. Ghannadi, M., Saadatseresht, M., &Eftekhary, A. (2014). Terrain height information extraction using TerraSAR-X images intensity component. Conferences of Geophysics, Iran, Tehran, 59-63.
12. Ghannadi, M. A., &Saadaseresht, M. (2018). A modified local binary pattern descriptor for SAR image matching. IEEE Geoscience and Remote Sensing Letters, 16(4), 568-572.
13. Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., &Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276-288.
14. Immitzer, M., Vuolo, F., &Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe.Remote Sensing, 8(3), 166.
15. Letsios, V., Faraslis, I., &Stathakis, D. (2019). InSAR DSM using Sentinel 1 and spatial data creation. In Proceedings of the 22th AGILE International Conference on Geographic Information Science (AGILE 2019).–Limassol: Cyprus University of Technology.
16. Li, Y., Martinis, S., Plank, S., & Ludwig, R. (2018). An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data.International journal of applied earth observation and geoinformation, 73, 123-135.
17. Li, Z. (1996). Multiresolution approximation of the gravity field.Journal of geodesy, 70(11), 731-739.
18. Lombardi, N., Lorusso, R., Fasano, L., &Milillo, G. (2016). Interferometric COSMO-SkyMed Spotlight DEM generation. Paper presented at the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
19. Makineci, H., &Karabörk, H. (2016). Evaluation digital elevation model generated by synthetic aperture radar data.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1.
20. Mallat, S. G. (1989). Multiresolution approximations and wavelet orthonormal bases of Transactions of the American mathematical society, 315.69-87, (1).
21. Mohammad Amin Ghannadi, MohanmmadSaadatseresht& Hamid Abrishamimoghadam.(2013). Evaluation of Radar Spaceborne Image Matching Methods.(MSc).University of Tehran.
22. Nie, J., Ren, H., Zheng, Y., Ghent, D., &Tansey, K. (2020). Land Surface Temperature and Emissivity Retrieval FromNighttime Middle-Infrared and Thermal-Infrared Sentinel-3 Images. IEEE Geoscience and Remote Sensing Letters.
23. Nikolakopoulos, K., &Kyriou, A. (2015). Preliminary results of using Sentinel-1 SAR data for DSM generation. European Journal of Geography, 6(3), 52-68.
24. Nonaka, T., Asaka, T., Iwashita, K., & Ogushi, F. (2019). The Relationships between Errors of Dem and the Height of Ambiguity of Sentinel-1.In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 1725-1728.
25. Sidney Burrus, C., Gopinath, R. A., &Guo, H. (1998). Introduction to wavelets and wavelet transforms. A Primer; Prentice Hall: Upper Saddle River, NJ, USA.J.
26. Ghannadi, M., &Saadatseresht, M. (2018). Efficient method for outlier removal in SAR image matching based on epipolar geometry. IET Radar, Sonar & Navigation, 12(11), 1307-1312.
27. Taghadosi, M. M., Hasanlou, M., &Eftekhari, K. (2019). Retrieval of soil salinity from Sentinel-2 multispectral imagery. European Journal of Remote Sensing, 52(1), 138-154.
28. Toming, K., Kutser, T., Uiboupin, R., Arikas, A., Vahter, K., &Paavel, B. (2017). Mapping water quality parameters with sentinel-3 ocean and land colour instrument imagery in the Baltic Sea. Remote Sensing, 9(10), 1070.
29. Wang, X., Liu, L., Shi, X., Huang, X., &Geng, W. (2018). a High Precision dem Extraction Method Based on Insar Data. In ISPAn, (4), 211-216.
30. Yagüe-Martínez, N., Prats-Iraola, P., Gonzalez, F. R., Brcic, R., Shau, R., Geudtner, D., &Bamler, R. (2016). Interferometric processing of Sentinel-1 TOPS data.IEEE Transactions on Geoscience and Remote Sensing, 54(4), 2220-2234.
31. Zhang, D., Wang, F., Huang, Z., Zhou, L., & Wu, Y. (2012). Reconstruction of InSAR DEM using ERS-1/2 interferogram and SRTM data. In Advances in Computational Environment Science, Springer, Berlin, Heidelberg, 129-136.