Abstract
The application of SAR interferometry (InSAR) in topographic mapping is usually limited by geometric/temporal decorrelations and atmospheric effect, particularly in repeat-pass mode. In this paper, to improve the accuracy of topographic mapping with high-resolution InSAR, a new approach to estimate and remove atmospheric effect has been developed. Under the assumptions that there was no ground deformation within a short temporal period and insignificant ionosphere interference on high-frequency radar signals, e.g. X-bands, the approach was focused on the removal of two types of atmospheric effects, namely tropospheric stratification and turbulence. Using an available digital elevation model (DEM) of moderate spatial resolution, e.g. Shuttle Radar Topography Mission (SRTM) DEM, a differential interferogram was firstly produced from the high-resolution InSAR data pair. A linear regression model between phase signal and auxiliary elevation was established to estimate the stratified atmospheric effect from the differential interferogram. Afterwards, a combination of a low-pass and an adaptive filter was employed to separate the turbulent atmospheric effect. After the removal of both types of atmospheric effects in the high-resolution interferogram, the interferometric phase information incorporating local topographic details was obtained and further processed to produce a high-resolution DEM. The feasibility and effectiveness of this approach was validated by an experiment with a tandem-mode X-band COSMO-SkyMed InSAR data pair covering a mountainous area in Northwestern China. By using a standard Chinese national DEM of scale 1:50,000 as the reference, we evaluated the vertical accuracy of InSAR DEM with and without atmospheric effects correction, which shows that after atmospheric signal correction the root-mean-squared error (RMSE) has decreased from 13.6m to 5.7m. Overall, from this study a significant improvement to derive topographic maps with high accuracy has been achieved by using the proposed approach.
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More From: ISPRS Journal of Photogrammetry and Remote Sensing
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