Abstract
Abstract. Spatiotemporal variations of pressure, temperature, water vapour content in the atmosphere lead to significant delays in interferometric synthetic aperture radar (InSAR) measurements of deformations in the ground. One of the key challenges in increasing the accuracy of ground deformation measurements using InSAR is to produce robust estimates of the tropospheric delay. Tropospheric models like ERA-Interim can be used to estimate the total tropospheric delay in interferograms in remote areas. The problem with using ERA-Interim model for interferogram correction is that after the tropospheric correction, there are still some residuals left in the interferograms, which can be mainly attributed to turbulent troposphere. In this study, we propose a Generative Adversarial Network (GAN) based approach to mitigate the phase delay caused by troposphere. In this method, we implement a noise to noise model, where the network is trained only with the interferograms corrupted by tropospheric noise. We applied the technique over 116 large scale 800 km long interfergrams formed from Sentinel-1 acquisitions covering a period from 25th October, 2014 to 2nd November, 2017 from descending track numbered 108 over Iran. Our approach reduces the root mean square of the phase values of the interferogram 64% compared to those of the original interferogram and by 55% in comparison to the corresponding ERA-Interim corrected version.
Highlights
Over the years, various satellites like ERS-1, ERS-2 and Envisat has been in use for the interferometric capability for a wide range of geophysical (Hooper et al, 2007), (Haghshenas Haghighi and Motagh, 2016), (Motagh et al, 2017), engineering (Fornaro et al, 2013) and environmental ((Castel et al, 2000)) applications
While conventional Interferometric Synthetic Aperture Radar (SAR) (InSAR) suffers from interference from unwanted signals like variations of scattering properties of the earth’s surface or atmospheric conditions through time (Hooper et al, 2012), multi-temporal interferometric methods (MTI) including Persistent Scatter InSAR (PSI) (Ferretti et al, 2001), (Hooper et al, 2004) and Small Baseline Subset (SBAS) ((Berardino et al, 2002)) present a specific class of processing that exploits multiple SAR images acquired over an area to separate the displacement signal from the unwanted noise
For our dataset of choice, we have focused on Iran and used the unwrapped differential phase derived by large scale interferograms of Sentinel-1 and the differential zenith total delay (ZTD) derived from the ERA-Interim model at the respective acquisition times as the interferograms
Summary
Various satellites like ERS-1, ERS-2 and Envisat has been in use for the interferometric capability for a wide range of geophysical (Hooper et al, 2007), (Haghshenas Haghighi and Motagh, 2016), (Motagh et al, 2017), engineering (Fornaro et al, 2013) and environmental ((Castel et al, 2000)) applications Utilizing these Synthetic Aperture Radar (SAR) acquisitions, repeated approximately from the same point in space at different times, Interferometric SAR (InSAR) gives us the differences in path length in the scale of the carrier wavelength, due to changes in wavelength (Massonnet and Feigl, 1998), (Burgmann et al, 2008).
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