Persistent scatterer interferometry (PSI) techniques using amplitude analysis and considering a temporal deformation model for PS pixel selection are unable to identify PS pixels in rural areas lacking human-made structures. In contrast, high rates of land subsidence lead to significant phase-unwrapping errors in a recently developed PSI algorithm (StaMPS) that applies phase stability and amplitude analysis to select the PS pixels in rural areas. The objective of this paper is to present an enhanced algorithm based on PSI to estimate the deformation rate in rural areas undergoing high and nearly constant rates of deformation. The proposed approach integrates the strengths of all of the existing PSI algorithms in PS pixel selection and phase unwrapping. PS pixels are first selected based on the amplitude information and phase-stability estimation as performed in StaMPS. The phase-unwrapping step, including the deformation rate and phase-ambiguity estimation, is then performed using least-squares ambiguity decorrelation adjustment (LAMBDA). The atmospheric phase screen (APS) and nonlinear deformation contribution to the phase are estimated by applying a high-pass temporal filter to the residuals derived from the LAMBDA method. The final deformation rate and the ambiguity parameter are re-estimated after subtracting the APS and the nonlinear deformation from that of the initial phase. The proposed method is applied to 22 ENVISAT ASAR images of southwestern Tehran basin captured between 2003 and 2008. A quantitative comparison with the results obtained with leveling and GPS measurements demonstrates the significant improvement of the PSI technique.
Precise leveling surveys across southwest of Tehran have revealed a significant subsidence due to the overexploitation of groundwater. In order to monitor the temporal evolution of the deformation, Interferometric SAR time series analysis was applied using ENVISAT ASAR images recorded between 2003 and 2005. Only Interferograms with small temporal baselines are processed to decrease the temporal decorrelation effect caused by the agricultural fields. However, the spatial baselines of the processed interferograms are not as small as in the conventional Small Baseline Subset (SBAS) method. Coherence analysis reveals that the spatial decorrelation is insignificant. However, since the constructed interferograms are affected by topographic artifacts caused by the large spatial baselines, a multi-step procedure was used in order to refine the interferometric phase. Smoothed time series analysis was then carried out to retrieve the atmospheric-error free deformation corresponding to every acquisition time. The mean displacement velocity map extracted from the time series results indicates a maximum subsidence rate of 24 cm/yr. Chronological sequence of the computed deformations for several points located in the subsidence area shows the permanent aquifer system compaction at a long-term constant rate on which the seasonal effects are superimposed. Sustained hydraulic head declines reveal a relatively low correlation with InSAR derived information. Comparison of the subsidence rate to soil type profiles in different parts of the subsidence area was then used to interpret the deformation signal.
The development of the polarimetric synthetic aperture radar (PolSAR) applications has been accelerated by coming of
new generation of SAR polarimetric satellites (TerraSAR-X, COSMO-SkyMed, RADARSAT-2, ALOS, etc.). The aim
of this article is to extract the information content of the polarimetric SAR data. Cross products of four channels "HH,
HV, VH, and VV" could be at least nine features in vector space and by applying the different class separability
criterion, the impacts of each feature, for extracting different patterns, could be tested. We have chosen the large distance
between classes and small distance within-class variances as our criterion to rank the features. Due to high mutual
correlation between some of the features, it is preferable to combine the features which result in the lower number of
features. Also the computational complexity will be decreased when we have lower number of features. Due to these
advantages, our goal would be to decrease the number of features in vector space. To achieve that, a subset of ranked
features consists of two to nine ranked features will be classified and the classification accuracy of different subsets will
be evaluated. It is possible that some of the new features that have been added to the old subsets change the classification
accuracy. Finally different feature subsets which were selected based on the various class-separability approaches will be
compared. The subset that gives the highest overall accuracy would be the best representative of the nine originally