2021 6th International Conference on Image, Vision and Computing (ICIVC) | 2021

Improved SAR DSM Generation Approach Using the High-Precise 1:10000 Scale Surveying and Mapping Optical DSM

 
 
 
 
 
 
 

Abstract


DSM is one of the important component of basic geographic information digital product which mainly acquired by the optical stereo image photogrammetry and SAR data interferometry, however, the interferometric strips of high-resolution SAR data was dense and very difficult to unwrap. In order to solve this problem, an improved phase unwrapping approach using the high-precise 1:10000 scale surveying and mapping optical DSM data was proposed in this paper. 13 pairs of SLC data for TanDEM-X image covering the eastern part of Xi an region were selected as the experimental image, and SRTM30 and 1:10000 high-precise optical DSM data distributed in this experimental region were collected as reference data. Moreover, a total of 393 outdoor surveying GCPs were used as check points to examine the accuracy of DSM generated by the traditional and improved SAR-DSM generation approach. In general, the RMSE of improved and traditional SAR-DSM generation approach was 7.19m and 9.31m, respectively. By the vertical accuracy examination of check points, for the improved and traditional SAR-DSM generation approach, the ratio of vertical error of 0-3m was 50.89% and 48.85%, the ratio of 3-5m was 13.23% and 15.01%, the ratio of 5-8m was 16.79% and 16.54%, the ratio of 8-14m was 11.71% and 13.49%, and the ratio of more than 14m was 7.89% and 5.6%, respectively. It can be seen from the comparison that the result of improved SAR-DSM generation approach was better than the traditional approach. Because the experimental areas and experimental images selected in this paper were large and representative, the improved SAR-DSM generation approach using the high-precise 1:10000 scale surveying and mapping optical DSM recommended in this paper can be used as a new method when generating DSM using the SAR interferometry technology.

Volume None
Pages 353-356
DOI 10.1109/ICIVC52351.2021.9527001
Language English
Journal 2021 6th International Conference on Image, Vision and Computing (ICIVC)

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