Digital elevation model (DEM) is an important geospatial data, which can provide a lot of topographic information. Currently, most commonly DEM data is derived from SAR images acquired before 2011, which has poor timeliness and is difficult to reflect the latest topographic features in many areas, especially in urban areas where the topographic features are rapidly updated. Therefore, it is necessary to explore DEM generation methods in urban areas to provide technical support for further studies on municipal planning and surface deformation in urban areas.This paper used two SAR images with bistatic mode, single-polarization (HH), and single look complex as experimental data from TerraSAR-X/TanDEM-X. The imaging time was January 30, 2013. The spatial baseline was 248.467 m. The research obtained good timeliness and high-resolution DEM of Maanshan area based on the iteration and fusion technology using two TerraSAR-X/Tandem-X SAR images and AW3D30 DEM from JAXA/EORC.With a comparison of 90m SRTM and 30 m AW3D30, this paper found that the DEM based on iteration and fusion technology could show more ground details, especially for the large single buildings at the sparse area. However, the extracted DEM at the dense-buildings area had the low reliability and still needed to further explore the solution, due to the obstruct of sensor line of sight and the distribution of shadow.In conclusion, this paper confirmed that it is feasible to obtain high-resolution and high time efficiency DEM using the iteration and fusion technology. It is able to improve the resolution of DEM and update the topographic from the original DEM by iteratively fused with AW3D30 to eliminate overlay and shadow..
CITATION STYLE
Gao, E. T., Lan, Y. P., Luo, Y., Ge, S. Y., Yu, H. M., & Lai, P. D. (2020). RESEARCH on HIGH PRECISON DEM of URBAN AREA BASED on TERRASAR-X/TANDEM-X. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 849–854). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-3-W10-849-2020
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