Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance Computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

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.

Advanced Differential SAR Interferometry and specifically Persistent Scatterers (PS) Interferometry have become popular techniques since they offer the possibility of large-scale analysis and monitoring of Earth's surface. In this work, we address the exploitation of widely used interferometric phase filtering method, SquueSAR, for the PS detection within a two-step processing scheme based on a sequence of low and high-resolution processing. We propose a modification of the iterative approach proposed in [1], in order to avoid phase shifts. The effects of the modification quantify in a significan improvement of the detection performances, verified on both simulated an real data.

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.

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.

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.

Synthetic Aperture Radar (SAR) tomography presents the advantage of multiple stable targets detection within same pixel. Fast-sup-GLRT (generalized likelihood ratio test based on support estimation) algorithm proved to be an ideal compromise between detection capabilities and computational complexity. In this work, a multi-look version of this detector which exploits the advantages of Capon estimation is examined. Statistical analysis of estimation and detection processes are conducted to compare the performances of sequential non-linear least-squares (NLLS) search and Capon filtering of projected data for double PS identification. Main objective is to exploit the super-resolution advantages of NLLS method without the risk of multiple stable targets classification from the same scattering contribution. For the last desiderate, an additional verification is included within the detection step.

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.

We present a solution for simplifying a recently proposed two-step processing technique that allows to retrieve the constant phase offset present in the unwrapped synthetic aperture radar (SAR) interferograms by exploiting an external, even low-accuracy, digital elevation model (DEM) of the illuminated area and without using corner reflectors. In particular, we show in this letter that the second processing step, namely, the slope-topography-based estimate, can be avoided without impairing the accuracy of the final phase offset estimate. To this aim, we introduce a simple modification to the first step, referred to as phase-based estimate, by considering the vertical bias of the available external DEM as the second unknown parameter in the carried out estimation. The simplified algorithm is very easy to implement and is particularly suitable for airborne SAR interferometry. It has been tested on real airborne SAR data and the obtained results show that the achieved accuracy is the same or better than that achieved through the original two-step approach.

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.

In order to generate a Digital Elevation Model (DEM) by exploiting the Synthetic Aperture Radar (SAR) Interferometry (InSAR) technique, it is necessary to properly estimate an unknown phase offset present in the unwrapped interferogram.

The paper presents the results of an experiment carried out with a hybrid along-track (AT) / across-track (XT) interferometric Synthetic Aperture Radar (SAR) configuration in order to simultaneously estimating the velocity and the elevation pattern of the illuminated sea surface. In particular, the experiment has been carried out in 2013 with the X-band InSAeS4 airborne SAR system, which is equipped with three antennas installed in such a way to allow the simultaneous presence of three AT baselines and three XT baselines. The test-site is the coastline stretch of the Campania region, in the South of Italy, close to the Volturno river.

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 Interferometry (InSAR) exploits the phase differences (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the InSAR processing chain, it is necessary to calculate a proper phase offset (PhOff) value to add to the unwrapped SAR interferogram. To this aim, different algorithms have been proposed in the literature, which typically exploit the external topography information provided by proper Ground Control Points (GCPs) relevant to the observed scene. In this paper we present a Maximum Likelihood (ML) approach for the InSAR PhOff estimation. It is based on the use of GCPs represented either by Corner Reflectors (CRs) or by the pixels of an available external Digital Elevation Model (DEM) of the observed area. The effectiveness of the proposed approach is assessed on simulated data.

InSAeS4 is an Italian X band airborne SAR system, recently upgraded to a multi-antenna interferometric configuration. The system is able to acquire single pass across-track and along-track multi-baseline data with different acquisition modes characterized by different geometric resolutions and across-track swaths. In recent work we have analyzed the high resolution InSAeS4 products (obtained by transmitting 400 MHz bandwidth pulses), characterized by the narrowest across-track swath (1.5 Km wide). In this work, we focus on the InSAeS4 products characterized by the widest across-track swath, at expenses of the lowest geometric resolution. To this aim we show some results achieved with an interferometric dataset acquired by the system in 2013 over the Napoli area, Italy.

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.

Digital elevation model (DEM) generation through interferometric processing of synthetic aperture radar (SAR) data requires the calculation of a constant phase offset present in the unwrapped interferograms. This operation is usually carried out by exploiting the external information provided by GPS measurements in correspondence of corner reflectors (CRs) properly deployed over the illuminated area. This is, however, expensive in terms of cost and time. Moreover, deployment of CRs along with the corresponding in situ GPS measurements can be difficult (if not impossible) in unfriendly areas or in natural disaster scenarios. To circumvent these limitations, we address in this work the estimation of the required phase offset by exploiting a low-accuracy external DEM, without using CRs. More specifically, a two-step approach is proposed. The first step exploits the synthetic phase computed by means of the external DEM and represents a straightforward extension of the procedure that is usually applied in the presence of CRs. Subsequently, in order to refine the achieved solution, a second step is introduced. It is based on a least squares approach that properly exploits the difference between the available low-accuracy DEM and the interferometric DEM generated by means of the phase offset value roughly estimated through the first step. The presented approach is very easy to implement and allows us to achieve an accurate and fast estimate of the needed phase offset, even in the presence of an external DEM affected by a vertical bias and/or a planar shift. The algorithm performances improve in the presence of a large variation of the look angle, as it generally happens in airborne systems. On the other side, the effectiveness of the algorithm may be impaired by the possible presence of artifacts in the unwrapped interferograms, such as those due to the residual motion errors typical of repeat-pass airborne SAR scenarios. Accordingly, the proposed solution is particularly suitable for single-pass interferometric airborne SAR systems, as demonstrated through the presented experimental results achieved on real data.

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.

TELAER is an Italian airborne X-Band Synthetic Aperture Radar (SAR) system recently upgraded to the interferometric mode thanks to an Italian National Research Council (CNR) funding. This system upgrading has been completed at the beginning of 2013 with a flight-test campaign carried out over the Napoli area, Italy. In this paper we present some results relevant to a single-pass interferometric dataset acquired during these flight-tests.

Synthetic Aperture Radar Interferometry (InSAR) allows the generation of Digital Elevation Models (DEMs) exploiting the phase difference (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the processing chain leading from the acquired SAR data pair to the final InSAR DEM, it is necessary to calculate a proper phase offset value to add to the unwrapped SAR interferogram. Moreover, in the airborne case, it can be necessary also the compensation of the residual motion errors not perfectly removed by the exploited Motion Compensation (MOCO) procedures during the focusing step. In this paper we present a novel algorithm for joint InSAR phase offset calculation and residual motion error compensation based on the use of an external low accuracy DEM. The effectiveness of the proposed algorithm is assessed on real data acquired by the multi-antena airborne OrbiSAR and TELAER system, both operating at X-band.

Accurate evaluation of the Antenna Phase Center (APC) position is a key issue in radar applications. In this work we propose an approach for the APC position measurement in anechoic chamber. The presented method allows overcoming some limitations of the classical algorithm known as Two Point Approach, and it has been tested on two X-band antennas: a standard horn antenna and the three-horn array antenna mounted onboard the TELAER airborne Synthetic Aperture Radar system.

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.

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.

In this work we focus on the Precise Topography- and Aperture-dependent algorithm, briefly PTA, proposed in the recent literature to remove the residual errors still affecting airborne SAR data focused with standard two-step Motion Compensation algorithms. More specifically, we first show that the azimuth-to-frequency mapping used by the PTA procedure is not fully appropriate, because it does not account for the presence of the residual motion errors themselves. Then, we show how to calculate the correct azimuth-to-frequency mapping able to fully account for the presence of the residual motion errors. Finally, we show how to modify the original PTA in such a way to properly exploit the correct azimuth-to-frequency mapping previously discussed.

Synthetic Aperture Radar Interferometry (InSAR) allows the generation of Digital Elevation Models (DEMs) exploiting the phase difference (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the processing chain leading from the acquired SAR data pair to the final InSAR DEM, it is necessary to calculate a proper phase offset value to add to the unwrapped SAR interferogram. To this aim, different algorithms have been proposed in the literature: most of them make use of very accurate information on the position of Ground Control Points (GCPs), such as Comer Reflectors (CRs), properly deployed over the observed scene. In this paper we present a novel algorithm for InSAR phase offset calculation without using CRs. The effectiveness of the proposed algorithm is assessed on real data acquired by the multi-antenna airborne OrbiSAR system operating at X-Band.

Airborne synthetic aperture radar (SAR) raw data are affected by motion errors. These are commonly accounted for via standard two-step motion compensation (MOCO) algorithm followed by the Precise Topography- and Aperture-dependent (PTA) procedure proposed some years ago. In this letter, we show how the azimuth-to-frequency mapping used by the PTA approach should be modified to fully account for the presence of uncompensated motion errors.

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.

This paper describes the capabilities of the TELAER X-Band airborne SAR system, which has been upgrading according to a Italian National Research Council (CNR) funding. Such a system upgrading consists first of all in the realization of a multi-antenna system able to carry out, simultaneously, Across-Track (XT) and Along-Track (AT) SAR Interferometry (InSAR). Moreover, the system upgrading is also aimed at improving the performances achievable in repeat-pass applications, such as Differential SAR Interferometry, (DInSAR), which require very precise measurement of the tracks described during the flight by the SAR antennas.

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.

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.

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.

Three-dimensional synthetic aperture radar (SAR) imaging, a technique also known as SAR tomography, uses multiple views to extend the capability of SAR systems to 3-D imaging by achieving a profiling of the scattering power at different heights. Multiple views are obtained with the current satellite technology via successive passes of a single antenna SAR sensor over the same scene, but next-generation sensor formations are foreseen to acquire multistatic data. Conventional processing, such as the beamforming, or singular values decomposition inversion is based on geometrical derivations and, hence, assumes the accurate phase calibration and the absence of target decorrelation. This paper analyzes the effects of phase miscalibration due to residual uncompensated atmospheric contribution and temporal decorrelation and proposes a 3-D imaging technique based on a linear minimum mean square error approach. The resulting algorithm extends the possibilities of the conventional processing by carrying out an integration of data that accounts for theaprioridata correlation properties. Hence, it allows handling of the presence of additional stochastic contributions such as: temporal coherence losses and atmospheric phase miscalibration. Moreover, with reference to future bistatic and multistatic systems, it permits an improved coherent integration of data acquired by simultaneous antenna in repeated passes.

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 is an extension of the concepts of SAR interferometry and differential interferometry SAR and offers new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting single scatterers for localization and monitoring issues. To this end, we resort to a constant false alarm rate (CFAR) detection scheme which can be synthesized according to three different design criteria: generalized likelihood ratio test, Rao test, and Wald test. At the analysis stage, the performance of the aforementioned detector is compared to that of a previously proposed CFAR scheme, based on the multi-interferogram complex coherence and widely used in persistent scatterer interferometry. The analysis is conducted both on simulated and on real SAR data, acquired by ERS-1/2 satellites. Finally, Cramer-Rao lower bounds for the estimation of the scatterer elevation and velocity are provided.

This work is focused on the detection of single and multiple scatterers in multiview/multitemporal SAR data in order to locate and monitor a high number of ground structures with low signal misinterpretation. This issue is addressed here by combining amplitude and phase data, differently from common techniques for the detection of single scatterers which rely on phase coherence measures. Experiments with real satellite C-band data are presented with both full resolution and multilook processing.

Enhanced spatial differences (ESD) is a new processing chain for monitoring ground deformations at small scale over wide areas. Core of the processing is the spatial differencing (SD) step that performs a quick preliminary estimation of the mean deformation velocity and residual topography via spatial differences. We show the results achieved by applying the ESD algorithm to data collected over adjacent tracks for ERS and ENVISAT.

We focus on the synthesis and the analysis of recursive receivers for direct-sequence code-division multiple-access systems aimed at achieving satisfactory performance at the price of a moderate computational complexity. At the analysis stage, some interesting properties shared by the proposed procedures are proven. Finally, the performance assessment shows that the new schemes are superior to the linear detectors, and some of them achieve a bit-error rate close to that of the optimum receiver.

This work addresses the derivation of the phase difference-based maximum likelihood (ML) phase unwrapping algorithm. To this end, we derive the joint statistics of the phase differences on a two-dimensional grid for the multichannel case, where several scaled wrapped phase values are available. Subsequently, we determine and study the structure of the phase difference-based ML estimator and compare it to known phase unwrapping techniques. This work allows us to frame single and multichannel algorithms in a common formulation. Moreover, among the known single-channel phase difference-based procedures, we identify those attaining an ML solution. We also show that multichannel phase difference-based and, recently proposed, phase-based ML algorithms achieve equivalent solutions.

This work addresses the derivation of a phase-difference (gradient) based maximum likelihood (ML) phase unwrapping algorithm. In particular, we determine and study the structure of the ML phase unwrapping on a 2D grid in the multi-channel case and compare it with existing phase unwrapping algorithms. This allows us first to frame single-channel, phase based phase unwrapping algorithm in a general formulation. Second, among the known single-channel, phase-difference based phase unwrapping algorithms we identify those achieving a ML solution. Although achieving results similar to existing phase-based ML phase unwrapping algorithm in the multi-channel case, our approach allows to easily incorporate possible a priori knowledge about the absolute phase variation dynamics.