Monitoring displacements with interferometric synthetic aperture radar involves, as final processing stage, the detection of persistent/distributed scatterers. Filtering and multilook is adopted to improve the detection performances in non-urbanized areas, where dominant point scattering mechanisms are less frequent. SqueeSAR and CAESAR based detectors were already compared in [1] under the hypothesis of weak and fully correlated scattering. In this work we extend the comparison to account for the presence of scattering decorrelation. The comparison is carried out by analyzing simulated data as well as data acquired by the COSMO-SkyMed constellation. In both cases the detection based on CAESAR outperforms the one based on SqueeSAR.

The development of SAR Interferometry for ground displacement monitoring has led to the implementation of operative services dedicated to the systematic delivery of archives of measurements. The exploitation of such data archives at regional/national scale requires frequently a proper post-processing of the measurements for their correct interpretation. Measurements are in fact provided in the radar line of sight and can be acquired by different angles, recently also with medium inclination orbits. In this work we consider the problem of projecting Differential Interferometric SAR measurements to the ground. We specifically develop a general methodology for linear (i.e., directional) and a planar projection of multi-orbit interferometric measurements, including its particularization to sloping terrains. Presented results highlight that the proposed projection methodology can provide useful and further options for the interpretation of the SAR displacement measurements with reference to the application to landslide monitoring.

The main problem related to Persistent Scatterer (PS) interferometry is the lack of large point clouds in rural areas, although it has proven to be a powerful tool in urban scenarios, especially in monitoring buildings with possible slow temporal deformations. The identification of PSs in low Signal to Noise Ratio (SNR) areas is crucial and can be done using a multiple hypothesis test to detect the possible presence of multiple scatterers [1]. In this paper, we frame this problem by exploiting the Kullback-Leibler Information Criterion (KLIC), developed in [2], to address the design of one-stage adaptive sensing architectures for multiple hypothesis testing problems in PS interferometry. Theoretical analysis shows the equivalence between the algorithm developed in [1] and [2] for a single scatterer. In this context we use the scheme of multiple hypothesis, provided in [2], for both the formalization of rural PS detection problem and its solution.

In this work, we propose a procedure for the estimation on the state of activity of rock glaciers in Argentinean Eastern Cordillera (Cordillera Oriental Argentina), based on the exploitation of displacement data provided by DInSAR multi-temporal processing. Additionally, we established a semi-automatic processing methodology to carry out a classification of the rock glaciers according to their measured displacement.

SAR interferometry is a well-established ground deformation monitoring technology that has recently enabled the implementation of projects dedicated to the systematic delivery of archives of deformation time series, even at the continental scale. The use of such archives of data for the management of the regional or national territory presumes the implementation of procedures aimed at converting the measurements products useful for the risk assessment. In this work we discuss preliminary results derived by trans-regional archives of deformation measurements for the characterization of slow-moving landslides. We specifically address the problem of implementing procedures aimed at filtering the data and estimating deformation components along the steepest slope direction.

In the last two decades, satellite Multi-Temporal Differential Interferometric SAR (DInSAR) techniques have become consolidated tools for studying slow-moving landslides. Moreover, the availability of sensors using different band frequencies and the development of advanced processing algorithms have constantly improved. In this work we discuss the first results of an experimental analysis carried out on multi-temporal multi-baseline L-Band data acquired by the newly launched SAOCOM satellites in the framework of the MEFISTO project founded by the Italian Space Agency (ASI), one of the main aims of which was to test the potentiality of multi-frequency (X, C and L bands) DInSAR data processed with different methodologies applied to a multi-scale slow-landslide analysis.

Synthetic Aperture Radar Interferometry (InSAR) with multiple passes is widely used to monitor deformations of the Earth surface. SqueeSAR and CAESAR (Component extrAction and sElection SAR) are two recently proposed solutions for performing an effective multi-acquisition multilook based filtering of interferograms to contrast the effects of phase noise for weak and/or decorrelating scatterers. Both methods can be used specifically at the detection stage in InSAR processing chains characterized by a two-stage/two-scale processing strategy, to increase the coverage of monitored points. In this work, we compare the detection performances of the SqueeSAR-based and CAESAR-based detectors. We address the evaluation of the performances on simulated data in the classical Gaussian scenario. Results of experiments on data acquired by the TSX and COSMO-SkyMED sensors are also included to discuss the advantages of one method over the other in real scenarios.

The integration of Differential Interferometric Synthetic Aperture Radar (DInSAR) displacement data with ground-based monitoring and ancillary multi-source information (deriving from geological-geomorphological-geotechnical analyses and damage surveys to the built-up environment) can nowadays provide a useful support to activities pursuing the management and protection of structures (e.g., buildings) and infrastructure (including roads, bridges, embankments) exposed to slow-moving landslide risk. Indeed, the implementation of the most suitable risk mitigation strategies requires appropriate procedures tailored on the quality and quantity of available datasets and their joint use according to the scope and the related scale of analysis. As a result of a 15-year multidisciplinary joint research activity, this paper presents a multi-scale methodology which is applied to buildings affected by slow-moving landslides located in selected areas of the Cosenza Province (Calabria region, southern Italy).

Among the several applications of multipass InSAR, monitoring of landslides represents one of the most challenging case because of the topographic characteristics, the heterogeneous displacement rates and the frequent presence of vegetation. In this study, we consider the use of multi-frequency InSAR data for the application to case studies located in the North and South Italy.

Basal and superficial displacements of two landslides in overconsolidated clayey soils are being monitored by inclinometers and GPS since 2005, and in some parts of the slope since the 1990s. The mostly North-South (N-S) orientation of the landslide displacements makes monitoring by satellite interferometry a challenging problem. To solve it, COSMO-SkyMed Differential Interferometric Synthetic Aperture Radar (DInSAR) data were analyzed at the scale for which inclinometers and GPS provide clear indication of displacement directions, thus allowing a confident use also of ERS and Envisat data. The kinematic history of the last 20 years of the urbanized area of the landslides was thus reconstructed even where ground-based displacement series were available over short periods or not available at all. The effectiveness of the remedial measures adopted in the area on displacement rates was also evaluated.

Decorrelation is a major drawback in Synthetic Aperture Radar (SAR) interferometry (InSAR) processing, causing poor density of measurement points, mainly outside of urbanized areas. SAR Tomography is one of the most suitable multi-temporal InSAR technique for built environment monitoring. Although typically capable of identifying a large number of scatterers, SAR Tomography detectors suffer the assumption of fully coherent scatterer response, which can be restrictive in real cases. In this work we address the test of a detector of partially correlated scatterers on a real COSMO-SkyMed (CSK) dataset, which was previously derived in the literature [1] as a Rao test for the single-look case and is here extended to the multi-look scenario. Experimental results are compared with those achieved by the Multi-look Generalized Likelihood Ratio Test (MGLRT). Both Partial Correlation and MGLRT have been implemented with a fixed multi-look degree.

Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations.

Interferometric and tomographic processing at full resolution of data acquired by very high resolution SAR systems allow providing accurate 3-D reconstruction of built environments, as well as deformation monitoring. In this framework, the detection of Persistent Scatterers (PSs) is a fundamental aspect. The exploitation of multiple observation looks (multilook), is an effective strategy which allows improving the coverage of monitored pixels in natural scenarios. In the framework of SAR tomographic processing, the Multilook Generalized Ratio Test (MGLRT) detection scheme has been proposed in literature. In this work we address the potentialities and main issues related to the exploitation of a multiresolution approach for MGLRT detection based on the identification of statistical homogeneous looks.

X-Band systems provide Very High Resolution (VHR) SAR data that have important applications in several areas of the risk monitoring. Processing of interferometric VHR SAR data stacks allows the accurate monitoring of deformation affecting buildings. In this work we discuss the application to the monitoring of the Latronico town, a village in South Italy, severely affected by the slow and continuous movement of a landslide. Data are relevant to the COSMO-SkyMed constellation and results are generated via a tomographic processing.

High and very-high resolution remote sensing data provided by the processing of Synthetic Aperture Radar (SAR) images via Differential Interferometry techniques (DInSAR) allow detecting ground surface displacements that can be used in monitoring/control of urban areas and integrated within procedures aimed at analyzing the vulnerability of affected facilities (e.g. structures and infrastructure). In this regard, this paper presents the results of a study that combines DInSAR data and information gathered from damage surveys to buildings interacting with slow-moving landslides. In particular, relationships between cause (DInSAR-derived differential settlements of buildings) and effect (recorded building damages) are retrieved and empirical fragility and vulnerability curves (to be used for damage forecasting purposes) are generated. Considering the encouraging outcomes, the adopted methodological approach can be successfully integrated within a more general framework for the management of slow-moving landslide risk to which urban areas are exposed.

In this work, we investigate the evolution of ground deformation in the Shanghai coastal region, China, over the last decade. We performed a Small BAseline subset DInSAR analysis based on the exploitation of three independent SAR datasets acquired by the ASAR/ENVISAT (ENV), COSMO-SkyMed (CSK), and Sentinel-1A (SlA) sensors, spanning the time frame between 2007 and 2017. More precisely, we used a time-dependent geotechnical centrifuge model of the subsidence signals to combine the available time-gapped CSK and ENV datasets, and hence we produced a long-term CSK, ENV, and SlA displacement time-series. The maximum subsidence rate calculated by the combined long-term ENV+CSK+S1A deformation time-series is about 30 mm/year. The achieved results are beneficial for future studies assessing the environmental risk (e.g., due to flooding) of the population living in the coastal area of Shanghai.

Persistent scatterer (PS) interferometry and more recently synthetic aperture radar tomography have shown to be powerful tools in urban scenarios for providing 3-D point clouds in the reconstruction of buildings as well as in the monitoring of their possible slow temporal deformations. The detection of PSs represents a fundamental aspect, which in the literature has been mainly addressed at full resolution (single-look detection), thus considering only the scatterer coherence properties along the different acquisitions. In this paper, we investigate the benefits offered by the usage of multiple observation looks. Multi-look generalized likelihood ratio test detection schemes are derived and analyzed in terms of detection performances. The analysis shows that even a slight multi-look can provide a dramatic improvement on the detection capability both on simulated and real data, especially in the areas characterized by a low signal-to-noise ratio and in the presence of a limited number of acquisitions.

Sparse Representation and Compressive Sensing (CS) theory has gained an increasing interest during the last years in many application fields, including SAR tomography. The latter offers the possibility to perform a focusing, beyond the classical 2D (azimuth-range) domain, along other dimensions: f.i., elevation and velocity. The literature, however, lacks of an assessment of the improvement of CS over classical point cloud detection schemes based on the Generalized Likelihood Ratio test which, in the simplest form, use basic beamforming (matched filter) detection based schemes. This work aims to provide a contribution along this line.

The paper summarizes the state-of-the-art and the current and future trends of advanced SAR interferometry and tomographic SAR, the latter also extended to the area known as multidimensional SAR imaging.

Interferometric and particularly tomographic processing at full resolution of data acquired by very high resolution SAR systems allows accurate 3D imaging and monitoring of buildings and infrastructures. Recently it has been shown that implementing a light data multilooking leads to a dramatical increase of the density of monitored pixels. In this work we investigate different options for performing the the multilook operation in SAR Tomography and we analyze their performances in terms of detection capabilities of single scatterers.

In order to retrieve information about the sea surface velocity in coastal Mediterranean regions from Synthetic Aperture Radar (SAR) data, in this work we exploit spaceborne as well as airborne data. In particular, we apply two different processing techniques. The first one exploits a single SAR image, whereas the second one, referred to as Along-Track SAR Interferometry, exploits SAR data pairs. The first technique has been applied to spaceborne ENVISAT-1 SAR data, whereas the second one has been applied to airborne data acquired by the Italian InSAeS4 X-band SAR system.

Synthetic aperture radar (SAR) tomography has been strongly developed in the last years for the analysis at fine scale of data acquired by high-resolution interferometric SAR sensors as a technique alternative to classical persistent scatterer interferometry and able to resolve also multiple scatterers. SqueeSAR is a recently proposed solution which, in the context of SAR interferometry at the coarse scale analysis stage, allows taking advantage of the multilook operation to filter interferometic stacks by extracting, pixel by pixel, equivalent scattering mechanisms from the set of all available interferometric measurement collected in the data covariance matrix. In this paper, we investigate the possibilities to extend SqueeSAR by allowing the identification of multiple scattering mechanisms from the analysis of the covariance matrix. In particular, we present a new approach, named “Component extrAction and sElection SAR” algorithm, that allows taking advantage of the principal component analysis to filter interferograms relevant to the decorrelating scatterer, i.e., scatterers that may exhibit coherence losses depending on the spatial and temporal baseline distributions, and to detect and separate scattering mechanisms possibly interfering in the same pixel due to layover directly at the interferogram generation stage. The proposed module allows providing options useful for classical interferometric processing to monitor ground deformations at lower resolution (coarse scale), as well as for possibly aiding the data calibration preliminary for the subsequent full-resolution interferometric/tomographic (fine scale) analysis. Results achieved by processing high-resolution Cosmo-SkyMed data, characterized by the favorable features of a large baseline span, are presented to explain the advantages and validate this new interferometric processing solution.

In the last years spaceborne Synthetic Aperture Radar (SAR) Tomography has been successfully exploited for the analysis of complex scenarios, such as urban areas and infrastructures. In this work a novel method is investigated to achieve improved multidimensional tomographic reconstructions of built structures at a fine scale: it exploits a preliminary data filtering stage carried out via the Component extrAction and sElection SAR (CAESAR) algorithm, based on the application of the Principal Component Analysis (PCA) with a reduced multilooking degree. Experimental results obtained by applying this technology to COSMO-SKYMED data are shown.

The work addresses the problem of focusing moving target in very high resolution Synthetic Aperture Radar (SAR) images. This task is here solved by using an Inverse Synthetic Aperture Radar (ISAR) add-on approach. The ISAR technique performs an autofocus procedure by implementing exhaustive search algorithms of functions based on image entropy or contrast. The algorithm discussed in this work is based on the re-use of autofocusing technique classically adopted in SAR focusing. The effectiveness of the proposed method is tested on COSMO-Skymed Spotlight SAR data of maritime targets.

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method.

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space/deformation-velocity (4-D) analysis. It extends SAR interferometry and differential interferometry concepts, offering new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting scatterers showing partial correlation properties induced by simultaneous acquisitions from satellite formations or an uneven temporal distribution of satellite constellations. To this end, we first provide a general model for the pixel imaged by a multidimensional SAR system. Then, we design a constant false alarm rate (CFAR) decision rule accounting for the presence of partially coherent scatterers. At the analysis stage, we assess the performance of the new detector also in comparison with a previously proposed CFAR scheme, developed in the context of SAR tomography for fully coherent scatterers.

Atmospheric propagation delay is a key error source in DInSAR processing and represents a main limitation to the use of DInSAR in emergency conditions. Current X-Band sensors, characterized by frequent passes, are even more affected by such problems. In this work we discuss a procedure based on the use of information extracted from GPS data.

In this paper a novel procedure for the filtering of interferograms based on the extension of SAR Tomography on multilook data is presented. This algorithm, named “Component extrAction and sElection SAR” (CAESAR) takes advantage of Principal Component Analysis to handle temporal and angular decorrelation effects as well as of SAR Tomography principles to solve the distributed layover at the interferogram generation stage. Results on Cosmo-Skymed real data confirm the effectiveness of the proposed procedure.

The new generation of synthetic aperture radar (SAR) sensors is providing images with very high spatial resolution, improved up to the meter scale. Such a resolution increase allows more accurate monitoring capabilities by means of interferometric approaches. The use of higher frequency enhances the sensitivity of the system even to minute changes, such as thermal dilations. This phenomenon has an impact on the interferometric products, particularly on the deformation velocity maps, if not properly handled. Man-made structures, such as steel core bridges and specific buildings, may be very sensible to thermal dilation effects. By extending the multitemporal differential interferometry SAR processing chains, in our case based on the multidimensional imaging (MDI) approach, an additional parameter related to temperature differences at acquisition instants, the thermal coefficient, can be accurately estimated. This parameter provides interesting perspectives in application to infrastructure monitoring: It brings information about the thermal behavior of the imaged objects. In this letter, we investigate the thermal response of the Musmeci bridge (Potenza, Italy), by experimenting the extended MDI approach on a real TerraSAR-X data set. Results highlight the possibility of such a technique to obtain measurements of the motion that is highly correlated with temperature, thus providing useful information about the static structure of bridges.

The new generation of synthetic aperture radar (SAR) sensors is providing images with very high spatial resolution, up to the meter scale. Such an increase of resolution allows a more effective monitoring of ground structures by means of interferometric approaches. SAR-tomography-based approaches use not only the phase but also the amplitude of the received data: they have shown better capabilities with respect to classical persistent scatterers interferometry approaches in monitoring ground scatterers in terms of detection and estimation accuracy and offer the possibility to resolve multiple scatterers. First results on TerraSAR-X data have demonstrated impressive capabilities in the reconstruction of single buildings and in the monitoring of their deformation. However, the use of higher frequency increases the sensitivity of the system even to minute changes such as thermal dilations. In this paper, we address extension of tomographic based approaches to the monitoring of structure thermal dilation. Aspects related to the coupling of estimated deformation parameters, and in general of the estimation accuracy, as well as problems of scatterers detection are deeply investigated. Results on real data are shown to demonstrate the capability of the technique to distinguish linear deformation and thermal dilation and to increase the quality of the monitoring, as well as to highlight coupling effects.

Markov Random Fields (MRF) are a powerful and wide adopted tool in image processing. MRFS together with Bayesian Estimation Theory can be used for detecting edges of man made structures in Synthetic Aperture Radar images. In this paper we present a method developed in the Markovian-Bayesian framework which is particularly suited to work in case of high coherence SAR interferometric images pairs, such as TanDEM-X Mission data. A real case study, consisting of a pair of complex TerraSAR-X images, is presented, showing the performances of the algorithm.

We present a research project, funded by the Italian Space Agency (ASI), aimed at performing 2D and 3D Focusing of COSMO/SkyMed (CSK) SAR Data. We describe the main objectives of the project, briefly illustrate employed techniques, and finally present the obtained results. The latter show that sub-meter resolution can be achieved in the enhanced spotlight CSK acquisition mode, and that by using 3D focusing it is possible to resolve scatterers at different slant heights within the same range-azimuth resolution cell, even in areas characterized by severe height discontinuities and large thermal dilations effects.

We present an adaptive filtering algorithm for accurate estimation of differential interferometric phase and coherence, in order to better support the SBAS approach. The typical method to enhance interferograms is a simple boxcar multilooking, that improves the signal-to-noise ratio (SNR) at the expense of the spatial resolution. The presented technique is based on an adaptive space filter that identifies and averages statistically homogeneous pixels in a given neighbourhood. It also consists of a phase filtering operation in the time domain via the temporal multilink for recovering the consistency of the interferometric phase (phase triangulation). Results on real Cosmo-SkyMed data, acquired over the city of Naples, are shown in order to demonstrate the effectiveness of the presented technique.

In this paper we discuss the extension of an algorithm for efficient processing of SAR data acquired in sliding spotlight mode based on a two-step approach. The two-step spotlight data processing procedure, already introduced in the literature, is a very simple and effective procedure because it is based on a plain use of classical (i.e. stripmap mode) SAR processing codes. The algorithm here proposed extends the two-step approach in such a way t account for the presence of squint. Experiments have been carried out on simulated data to clarify the rationale of the method and to confirm its validity. Results obtained by processing real data of the COSMO-SKYMED constellation are also presented.

The differential synthetic aperture radar interferometry (DInSAR) technique has dramatically boosted the application of remote sensing in many geophysical disciplines, particularly tectonics. Coseismic interferograms have provided, in many cases, “images of earthquakes,” showing the surface displacement due to the deep fault dislocation. Aside from being visually appealing, such interferograms are of fundamental importance for the analysis, by means of appropriate dislocation models, of the geometrical and kinematic characteristics of the fault, which are also parameters of key interest for earthquake risk management. This paper provides a contribution in the general framework of the integration of dislocation models in DInSAR processing. In particular, with reference to coseismic interferograms, it proposes a technique that allows the direct inversion of wrapped interferograms for dislocation model analysis. This option makes it possible to avoid the critical and error-prone processing step of phase unwrapping, carried out in the classical analysis of interferometric data. Examples of inversion of real data, relevant to the 1999 Athens and Izmit earthquakes, demonstrate the feasibility and the advantages of this data processing approach.

Synthetic aperture radar (SAR) tomography is a technique that extends the concept of SAR interferometry for the accurate localization and monitoring of ground scatterers. Data that are being acquired by the new high resolution SAR sensors offer new perspectives in the 3-D reconstruction and monitoring of urban areas and, particularly, of individual buildings. SAR tomography allows increasing the density of measurements by handling situations where multiple stable scatterers interfere in the same resolution cell. The detection of reliable, i.e., persistent, scatterers is however a challenging issue. In this paper, we investigate three detection approaches: The first is based on a modification of information theoretical criteria; the last two are based on the generalized likelihood ratio test. Theoretical performances are analyzed in details on simulated data, and results of the application to real data from both medium and very high resolution sensors are also provided.

Layover is frequent in imaging and monitoring with synthetic aperture radar (SAR) areas characterized by a high density of scatterers with steep topography, e.g., in urban environment. Using medium-resolution SAR data tomographic techniques has been proven to be capable of separating multiple scatterers interfering (in layover) in the same pixel. With the advent of the new generation of high-resolution sensors, the layover effect on buildings becomes more evident. In this letter, we exploit the potential of the 4-D imaging applied to a set of TerraSAR-X spotlight acquisitions. Results show that the combination of high-resolution data and advanced coherent processing techniques can lead to impressive reconstruction and monitoring capabilities of the whole 3-D structure of buildings.

COSMO/SKYMED is currently the unique constellation of synthetic aperture radar (SAR) sensors operative, which is also for civilian use. On April 6, 2009, an Mw 6.3 earthquake struck the city of l'Aquila in Central Italy. The constellation acquired data stacks over the hit area at an unprecedented temporal rate. In this letter, the results obtained by processing several data set via two independent multitemporal differential interferometric SAR techniques are presented to demonstrate the capability of this constellation in postseismic deformations monitoring.

Multitemporal differential interferometric synthetic aperture radar analysis is of fundamental importance in the monitoring of Earth surface displacements. In this context, a key role for the reconstruction of the deformation maps and time series is played by the phase unwrapping (PhU) that reconstructs the unrestricted phase signals starting from the measured wrapped versions, i.e., the interferograms. PhU is typically carried out independently for each interferogram in the 2-D azimuth-range domain via the efficient minimum cost flow (MCF) optimization technique. Recently, it has been proposed a two-step (TS) strategy that exploits both the temporal and the spatial structures of the available interferograms. The MCF algorithm is applied in this case also in the temporal/spatial baseline domain, and this step is combined with the classical 2-D space unwrapping. However, the restriction on the use of the MCF algorithm in the baseline domain poses limitations on the interferogram generation scheme. We present a formulation which makes use of the overdetermined nature of the operator that relates the phase differences to the absolute phase values: the problem is addressed in a more general framework that can cope with the 3-D (2-D space and time) nature of the data. This formulation is derived with reference to the sequential (TS) approach to overcome its restrictions on the interferogram generation. The new algorithm is validated on both simulated and real data. Moreover, the use of this new formulation for a full 3-D unwrapping is also addressed.

On April 6th2009 a Mw=6.3 earthquake struck the area around the city of L'Aquila in Italy. SAR systems have been proven to be valuable sensors for analyzing the effect of earthquakes and monitoring post-seismic displacements. Due to the low deformation rate, the study of post-seismic events requires the use of a multi-temporal InSAR approach. COSMO/SKYMED is a constellation of SAR sensors of 4 X-band sensors operative also for the civilian use. Thanks to the availability of a stack of ascending acquisitions, ad hoc programmed by ASI on the area stricken by the earthquake, it was possible to provide post-seismic deformation maps by using two different multi-temporal interferometric approaches: the SPINUA and SBAS techniques. The work is carried out in the framework of the MORFEO project dedicated to the monitoring of the landslides risk by means of Earth Observation data. The displacement maps related to the post-seismic activity are presented and commented. The results clearly show the potentiality of the COSMO/SKYMED constellation use for emergency monitoring.

In the last years MultiDimensional (3D and 4D) Synthetic Aperture Radar (SAR) techniques, also known as SAR tomography and differential SAR tomography, are emerging in the field of coherent combination of multibaseline/multitemporal SAR data. With respect to the classical differential interferometric processing, these techniques improve the capability of detection and monitoring of the ground targets. Moreover they were proven to be effective in resolving the signal interference due to the layover effect, that may occur in areas with high density of scatterers located on vertical structures, such as urban areas. Beside the development of these advanced techniques the new generation of sensor, such as TerraSAR-X and COSMO-SKYMED with very high spatial resolution offer new perspectives in the imaging and monitoring of urban areas. In this paper we address the application of the SAR tomography to real spaceborne data. Particularly, we show and discuss the first results of the application of this technique to high resolution TerraSAR-X data.

Four-dimensional synthetic aperture radar (SAR) imaging, also known as differential SAR tomography, is a new research topic in the framework of coherent multitemporal/multibaseline SAR processing that extends the interferometry concept. Four-dimensional SAR imaging-based processing could improve the capability of ground-scatterer monitoring with respect to classical differential interferometric SAR processing. The first results on the applicability of such an advanced tomographic SAR processing to real spaceborne data were recently discussed in the literature. In this letter, we present the results of an experiment with a data set that demonstrates the potentialities of this new technique for monitoring complex targets, such as infrastructures.

Phase unwrapping is a key step in DInSAR stack analysis: it allows restoring the phase values starting from the restricted ones to extract the deformation time series of the observed area. Most of the available interferogram stack phase unwrapping techniques unwrap each interferogram separately. Based on a strategy proposed in the literature, in this work we discuss an unwrapping procedure that exploits both the temporal and spatial characteristics of the interferogram stack. In particular we propose a technique that allows carrying out a temporal unwrapping step with a generic set of interferograms. The validation of our method is carried out by processing simulated data.

Multi-Dimensional (3D/4D) SAR imaging (SAR Tomography and Differential SAR Tomography) allows the localization and monitoring of ground scatterers, even interfering in the same azimuth-range pixel. Indeed, the presence of multiple scatterers has shown to affect even the performances of high resolution radar systems. In this paper we discuss two strategies for the detection of interfering scatterer pairs. The first one is based on the extension of the GLRT test already proposed for the detection of single scatterers, the second one is based on the BIC criteria commonly used in the contest of model order selection. Performances of the two decision schemes are evaluated on simulated data.

In this letter, an analytical description of the 2-D and 3-D imaging of ground-based synthetic aperture radar data is given. The ability of the 3-D imaging to separate targets along the elevation direction will also be shown, thus allowing their complete localization in space. The validation of the proposed method is done by exploiting simulated data.

The superposition of contributions from different stable targets within the same pixel is a phenomenon that may impair the imaging and monitoring of ground scatterers via the multipass synthetic aperture radar (SAR) interferometry technique. Three-dimensional SAR imaging, also known as SAR tomography, uses multiple views to profile the scattering power at different heights. This technique has been shown to be capable of separating interfering target responses on real data. Differential SAR tomography has been recently proposed as a technique that extends the potentialities of SAR tomography to the target deformation monitoring. It performs a 4-D space-velocity imaging that enables not only separating interfering targets in elevation but also distinguishing their single slow deformation velocities. This work addresses for the first time the application of 4-D SAR imaging to real data to determine the height and mean deformation velocity of single scatterers and double-scattering mechanisms interfering at high resolution in the same pixel. It also discusses the postprocessing steps required to identify the presence of stable single and double scatterers after elevation-velocity focusing. Moreover, it proposes a technique for the extraction of time series from interfering targets to measure possible nonlinear temporal deformations.

4D (space-velocity) SAR focusing is applied to real data acquired over urban areas to determine the position, in 3D, and the deformation velocity of single and double scatterers interfering, at high resolution, in the same azimuth-range pixel. We discuss the procedure required to identify the presence of stable single and double scatterers after the elevation-velocity focusing. Moreover we present a technique for the separation of time series associated with interfering scatterers to measure their possible non linear temporal deformations.

Ground-based SAR systems play a key role in active microwave remote sensing in many areas of environmental risk monitoring. Real time capability and flexibility make ground-based SAR suitable for monitoring in emergency cases, such as sudden landslide or volcanic activities. In this work we propose a 3D SAR imaging, for a ground-based stepped frequency radar, based on a tomographic technique. This method allows separate scattering mechanisms associated at target interfering in the same resolution cell.