The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 175–177, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 175–177, 2015

  10 Dec 2015

10 Dec 2015


M. Esmaeili1 and M. Motagh2 M. Esmaeili and M. Motagh
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2Department of Geodesy and Remote Sensing, GFZ German Research Center for Geosciences, Potsdam, Germany

Keywords: Polarimetric Optimization, Persistent Scatterer Candidate, PSInSAR, Amplitude Dispersion Index (ADI)

Abstract. Persistent Scatterer Interferometry for SAR data (PSInSAR) improves the ability of conventional InSAR time-series methods by detecting and analysing pixels where the portion of spatiotemporal decorrelations on the phase is negligible. Using dual/quad polarized SAR data provide us with an additional source of information to improve further the capability of InSAR analysis. In this paper, we present a method to enhance PSInSAR using polarimetric optimization method on multi-temporal polarimetric SAR data. The optimization process has been implemented to minimize the Amplitude dispersion Index (ADI) of pixels in SAR images over the time based on the best scattering mechanism. We evaluated the method on a dataset including 17 dual polarization SAR data (HH/VV) acquired by TerraSAR-X data from July 2013 to January 2014 over Tehran plain, Iran. The area has been affected by high rate (> 20 cm/yr.) of surface subsidence due to groundwater overexploitation. The effectiveness of the method is compared for both agricultural and urban regions affected by land subsidence. Furthermore single pole and optimized polarization results are compared together and with external observations from GPS measurements. The results reveal that using optimum scattering mechanism decreases the ADI values in urban and non-urban regions and increase the PS Candidate pixels (PSC) about three times and subsequently improves the PS density about 50% more than using single channel datasets.