ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 57–64, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-57-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 57–64, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-57-2021

  17 Jun 2021

17 Jun 2021

USING GENERATIVE ADVERSARIAL NETWORKS FOR EXTRACTION OF INSAR SIGNALS FROM LARGE-SCALE SENTINEL-1 INTERFEROGRAMS BY IMPROVING TROPOSPHERIC NOISE CORRECTION

B. Ghosh1, M. Haghshenas Haghighi2, M. Motagh1,2, and S. Maghsudi3 B. Ghosh et al.
  • 1GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing, 14473 Potsdam, Germany
  • 2Institut für Photogrammetrie und GeoInformation (IPI), Leibniz Universität Hannover, Nienburger Str. 1, D-30167 Hannover, Germany
  • 3Computer Science Department, Maria-von-Linden-Straße 6, 2nd floor, room no. 20-5 / A19, University of Tübingen, 720740 Tuebingen, Germany

Keywords: Sentinel-1, Interferometric Synthetic Aperture Radar (InSAR), ERA-Interim, GACOS, Generative Adversarial Network (GAN)

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.