TY - JOUR

T1 - Using generative adversarial networks for extraction of insar signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correction

AU - Ghosh, B.

AU - Haghighi, M. Haghshenas

AU - Motagh, M.

AU - Maghsudi, S.

PY - 2021/6/17

Y1 - 2021/6/17

N2 - 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.

AB - 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.

KW - ERA-Interim

KW - GACOS

KW - Generative Adversarial Network (GAN)

KW - Interferometric Synthetic Aperture Radar (InSAR)

KW - Sentinel-1

UR - http://www.scopus.com/inward/record.url?scp=85113173752&partnerID=8YFLogxK

U2 - 10.5194/isprs-annals-V-3-2021-57-2021

DO - 10.5194/isprs-annals-V-3-2021-57-2021

M3 - Conference article

AN - SCOPUS:85113173752

VL - 5

SP - 57

EP - 64

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9042

IS - 3

T2 - 24th ISPRS Congress on Imaging today, foreseeing tomorrow, Commission III

Y2 - 5 July 2021 through 9 July 2021

ER -