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Abstract-In previous works, a general framework to exploit polarimetric diversity to optimize the results of Persistent Scatterers Interferometry (PSI) was presented, but tested only with dual-pol data. In this paper, the performance of these algorithms is assessed using fully polarimetric data, acquired by Radarsat-2 satellite over the urban area of Barcelona (Spain). In addition, two new highly efficient polarimetric optimization methods, named MIPO and JDPO, are introduced and evaluated. Given the variety of dual-pol configurations provided by current polarimetric satellites, such as TerraSAR-X and Radarsat-2, and the upcoming launch of Sentinel-1, ALOS-2 and Radarsat Constellation Mission, a study has been also carried out in order to determine the best performing dual-pol configurations for polarimetric PSI. Subsidence maps of the area of study are computed for single-pol, dual-pol and full-pol data, which show the increase of density of pixels with valid deformation results as more polarimetric information is made available. In particular, for full-pol data we get an increase of up to 2.5 times more pixels for coherence-based PSI techniques (degraded-resolution), and over 4 times more for amplitude-based approaches (fullresolution), in comparison with single-pol data. Both higher density and quality of pixels yield better results in terms of coverage and accuracy.

Abstract-Persistent Scatterers Interferometry (PSI) techniques are designed to measure ground deformations using satellite Synthetic Aperture Radar (SAR) data. They rely in the identification of pixels not severely affected by spatial or temporal decorrelation, which in general correspond to pointlike, persistent scatterers (PS) commonly found in urban areas. However, in urban areas we can find not only PS but also distributed scatterers (DS) whose phase information may be exploited for PSI applications. Estimation of DS parameters require speckle filtering to be applied to the complex SAR data, but conventional speckle filtering approaches tend to mask PS information due to spatial averaging. In the context of single-pol PSI, adaptive speckle filtering strategies based on the exploitation of amplitude temporal statistics have been proposed which seek to avoid spatial filtering on non homogeneous areas. Given the growing interest on Polarimetric PSI techniques, i.e. those using polarimetric diversity to increase performance over conventional single-pol PSI, in this work we propose an adaptive spatial filter driven by polarimetric temporal statistics, rather than single-pol amplitudes. The proposed approach is able to filter DS while preserving PS information. In addition, a new methodology for the joint processing of PS and DS in the context of PSI is introduced. The technique has been tested for two different urban datasets: 41 dual-pol TerraSAR-X images of Murcia (Spain) and 31 full-pol Radarsat-2 images of Barcelona (Spain). Results show an important improvement in terms of number of pixels with valid deformation information, hence denser area coverage.

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