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Table of contents
1 Overview
1.1 Introduction
1.2 Main objective
1.3 Outline of this thesis
2 Background
2.1 Synthetic Aperture Radar
2.1.1 Acquisition system and geometry
2.1.1.1 Image Doppler centroid
2.1.1.2 SAR image distortions
2.1.2 The phase of the SAR signal
2.1.2.1 The travel phase
2.1.2.2 The reflection phase
2.1.2.3 The construction phase
2.2 SAR interferometry
2.2.1 Phase stability conditions
2.2.2 Estimation of the interferometric phase quality
2.2.3 Sources of decorrelation
2.2.3.1 Geometric decorrelation
2.2.4 Phase unwrapping
2.2.4.1 Residue-cut algorithm methods
2.2.4.2 Least squares estimation techniques
2.2.4.3 Other algorithms
2.2.5 SAR interferometry processing steps
2.2.5.1 Data extraction
2.2.5.2 SAR focusing
2.2.5.3 Generation of a descriptor of the illuminated ground surface
2.2.5.4 Correction of the product annotation timings
2.2.5.5 Image coregistration
2.2.5.6 Generation of the interferogram
2.2.5.7 Compensation of the orbital state vectors inaccuracies
2.2.5.8 Phase unwrapping
2.2.5.9 Geocoding
2.3 InSAR main applications
2.3.1 Digital Elevation Model generation
2.3.1.1 Geometric interpretation
2.3.2 Estimation of ground deformation maps
2.3.2.1 Complete interferometric phase model
2.3.2.2 Detection of movement
3 Persistent Scatterers Interferometry
3.1 Review of PSI technology
3.2 What is a PS
3.2.1 Why not all targets exhibit a PS behavior?
3.2.1.1 Type of reflection As it was highlighted above, different kinds of reflection can occur, as depicted in figure 3.2.2
3.2.1.2 Ground object size
3.2.1.3 Wavelength
3.2.1.4 Radar resolution
3.2.1.5 Functional models
3.2.2 Estimating PS like pixels on radar images
3.2.2.1 SAR amplitude stability
3.2.2.2 Stacking of the interferometric coherence
3.2.2.3 Other methodologies
3.3 Stable Point Network technique
3.3.1 Image extraction procedure
3.3.2 Image selection procedure
3.3.3 Image coregistration procedure
3.3.4 Initial mask of PS procedure
3.3.5 Stable Point Network analysis procedure
3.3.5.1 Relationship establishment
3.3.5.2 Estimation of the model parameters at network arcs
3.3.5.3 Network integration
3.3.5.4 Estimation of the Atmospheric Phase Screen (APS)
3.3.5.5 Final spatial high resolution estimation of the SPN model parameters
3.3.5.6 Estimation of the deformation time series
3.3.6 Final selection of points of measurement
3.3.6.1 Methodology for selecting good SPN points of measurement
3.3.7 PS gecoding
3.3.7.1 Precise geocoding procedure
3.3.7.2 Example of application
4 Improvements of the SPN technique
4.1 Image coregistration quality control
4.1.1 Detection of super PS
4.1.1.1 The impulse response function (IRF)
4.1.1.2 Identification of super PSs
4.1.2 Evaluation of the coregistration accuracy
4.1.3 Example of application of the methodology
4.2 PS-like pixel selection enhancements
4.2.1 Enhancements of the initial estimation of PS
4.2.1.1 Relative calibration of the images
4.2.1.2 Example of application
4.2.2 Final selection of PSlike pixels enhancements
4.2.2.1 Origin of ambiguous SPN measurements
4.2.2.2 Impact of SAR artifacts on SPN measurements
4.3 SPN linear deformation improvements
4.3.1 Robustness of the linear deformation pattern in time
4.3.1.1 Variogram definition
4.3.1.2 Estimation of the variogram per SLCs
4.3.1.3 Application
4.4 SPN non-linear
4.4.1 Characterization of the SPN linear model fitting procedure by means of simulations
4.4.1.1 Performance of the estimator in function of the noise
4.4.2 SPN estimation system for non-linear deformations
4.4.2.1 Example of monitoring of non-linear deformations by using a priori information: Katrina hurricane test case
4.4.2.2 Guidelines for detecting possible non-linear deformation areas in SPN
4.4.2.3 Advanced SPN for the automatic monitoring of non-linear deformations
4.4.2.4 Application on Paris test site
5 Summary and Conclusions
