Conventional geodetic methods rely on point measurements, which have drawbacks in detecting and tracking geologic disasters at specific locations. In this study, the estimation of ground surface deformation in economically important urban regions of Pakistan's southern coast from 2017 to 2022 is performed using interferometric SAR technique. Vertical displacement which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience annual subsidence of 130 mm, and the western less populated region experiences an uplift of 70 mm annually. The densely populated areas of the study area experience an annual subsidence of 130 mm, and the less populated western region experiences an uplift of 70 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. The results obtained on a Sentinel-1 SAR dataset indicate that there is significant subsidence in the major urban districts. A linear PS-InSAR approach is required to address the ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region.

Timely identification of floods is crucial for preserving lives and assessing damage. To ensure effective flood protection, it is imperative to have consistent real-time mapping of flooded regions. Floods often occur in specific weather conditions, accompanied by cloud cover due to excessive precipitation. Remote sensing, primarily using multi-spectral imagery from optical sensors and backscatter data from synthetic aperture radar (SAR), plays a vital role in delineating flood extents. SAR remote sensing, known for its all-weather capabilities, is widely adopted for flood mapping, providing continuous day and night coverage. This study utilized Sentinel-1 satellite datasets to map floods across 11 events, employing the DB-SEN1FloodNet model, achieving outstanding performance metrics.

Synthetic Aperture Radar Tomography has been an active research field for the estimation of both artificial structures and forest heights. Most spectral analysis approaches explore the covariance matrix (CM) to provide accurate reflectivity profiles. In practical situations, the statistical CM is unknown, hence several methods replace it with the sampling CM. Unfortunately, the latter does not always meet the application requirements. At our end, we propose to apply the Expectation Maximization algorithm to iteratively estimate the pseudo spectrum and then update the CM. The impact the proposed method has on height estimation over vegetated areas is carried out. Experimental results on a real dataset acquired by an airborne system show the effectiveness of the Expectation Maximization estimator in terms of ground and canopy discrimination for each polarization channel.

The building height estimation of urban environments is a challenging problem for Synthetic Aperture Radar (SAR). SAR Tomography (TomoSAR) conducts a series of acquisitions to realize a 3D reconstruction. Classical 3D focusing algorithms’ performance tends to be affected by the limited number of acquisitions, and the uneven baselines. Inspired by the advanced performance of TSNN on forest height estimation, in this study, we apply TSNN to reconstruct building height and we compare the obtained results with a classical Tomography approach. The experimental results are based on the data acquired by the DLR’s ESAR sensor at L-band over Dresden, Germany. The results illustrate the possibility of using the deep learning-based approach for building height estimation on urban environments.

The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.

The reconstruction of high-elevation natural and artificial structures through Synthetic Aperture Radar (SAR) tomography has been an active research topic owing to its significance in various earth science applications. However, the complexity of this task arises from inaccuracies in the estimated reconstruction, attributed to factors such as low signal-to-noise ratios, decorrelations, few and uneven measurements, and overlapping scatterers. The utilization of iterative spectral estimation methods has been demonstrated to be beneficial in addressing some of these inaccuracies. Thus, selecting the best method within this class constitutes a challenge. In this context, our letter aims to propose a generalized formula linking the maximum likelihood-based iterative methods via a regularization parameter. The behavior of the latter is analyzed for several values in order to unveil the potential of the proposed approach in achieving a balance between noise reduction and detection performance. The experimental study has been conducted on simulated and real SAR data acquired by airborne and spaceborne systems covering tropical forest and build-up areas. The obtained results show the effectiveness and performance of the optimal regularization parameter to eliminate noise while preserving scatterers’ contribution.

Surface deformation caused by an earthquake is crucial for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. On September 24, 2019, an earthquake with a magnitude of 5.6 Mw and a depth of 10 km struck Mirpur, Pakistan, causing significant damage. The study area is already facing numerous problems due to natural hazards, and the additional surface deformations caused by this earthquake have further increased its vulnerability. The objective of this study was to estimate the surface deformation associated with the earthquake. InSAR analysis was applied to 10 Sentinel-1A SAR images captured between July 30, 2019, and October 22, 2019, resulting in the generation of 7 interferograms that provided information on ground displacement caused by the earthquake. The estimated deformation range showed approximately -8 cm of subsidence and a 20 cm uplift of the surface along the line of sight (LOS). Vertical deformation was also estimated to range from -3 cm to 17 cm.

Multi-baseline multi-temporal Synthetic Aperture Radar (SAR) techniques such as SAR Tomography (TomoSAR) are affected by different forms and sources of noise. Its presence in complex-valued interferograms alters the reliability and accuracy of height estimation results. Thus, the key challenge of TomoSAR is to identify scatterers interfering within the same resolution cell. To this aim, we propose a spectral contextual filtering method based on subbands decomposition to reduce noise influence and improve the quality of the interferometric product for TomoSAR application. The impact of the proposed pre-processing approach on the Generalized Likelihood Ratio Test-based detection is carried out. Experimental results on data acquired by TerraSAR-X sensor show the performances of the denoising method to increase the detection capabilities.

Elevation estimation of canopy and ground is one of the main aims in dealing with forest scenario using Synthetic Aperture Radar (SAR) Tomography. Theoretically, SAR Tomography (TomoSAR) provides layover solution, allowing to reconstruct the elevation of the different contributions collapsing in the same resolution cell. TomoSAR is commonly applied on both urban and vegetated areas. Within the latter scenario, one of the most interesting outcomes of TomoSAR is the possibility of separating the canopy and ground, allowing the reconstruction of their height maps. Within this paper, we propose a Deep Learning (DL) based method for TomoSAR. In particular, a neural network was trained for predicting the elevation value of canopy and ground of an area under investigation, based on a stack of SAR fully polarimetric multi-baseline acquisitions. The method uses the Light Detection And Ranging (LiDAR) data as reference and exploit a classification approach. The process was operated on a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana. Testing results on real data are presented showing interesting results.

In this paper a Polarimetric SAR Tomographic (Pol-TomoSAR) technique, exploiting the local contextual information in small neighborhoods surrounding each range-azimuth pixel, is applied to fully polarimetric multi-baseline images of an urban area. In particular, the considered technique is based on the assumption that the pixels belonging to a neighborhood are located on a locally planar surface, whose slopes can be estimated for each range-azimuth pixel from the surrounding neighboring data. This assumption well adapts to urban areas, where the height profile can be well approximated by planes. In this way, the correlation among the heights of neighboring pixels can be taken into account for improving the accuracy of the height profile reconstruction. Preliminary results on fully polarization SAR data are presented.

This paper proposes to apply the local-plane model in urban tomography imaging to increase the detection probability and the regularity of the persistent scatterers (PSs). A local-plane generalized likelihood ratio test (LP-GLRT) algorithm is developed, which shows a better adaption to the nonplanar architectures and terrain when compared with the Multi-look GLRT algorithm. Experiments on Terra-SAR images are presented to validate the algorithm.

Synthetic Aperture Radar (SAR) tomographic techniques enable the reconstruction of the scene scattering structure along the vertical direction and can provide the temporal evolution of a cloud of reliable points located in the 3D space. The use of Generalized Likelihood Ratio Test approaches have been shown to be effective in selecting reliable multiple scatterers. Recently regularized tomographic methods have been proposed for increasing the density of the recovered scatterers in urban environments. This paper discusses the differences between these two approaches and performs a comparison of reconstruction results obtained from a stack of TerraSAR-X images, in a region of interest located in the city of Paris, France.

Backscattering separation coming from ground and canopy is one of the main aims in dealing with forest scenario using synthetic aperture radar (SAR) tomography. Theoretically SAR tomography (TomoSAR) provides layover solution, but in practice, insufficient vertical resolution using typical reconstruction approaches may not be sufficient for identification of the vertically aligned scatterers. To cope with this intrinsic issue, we proposed a method that separates the ground and canopy backscatterings based on Random- Volume-over-Ground (RVOG) model and by employing the generalized likelihood ratio test (GLRT) detection schemes over the covariance matrix. Such a separation allows identification of interference of the backscattering, which simply brings the possibility to resolve and separate ground and canopy superposition in the tomogram. Experimental validation of the proposed methodology is provided using a real data set acquired by the ONERA SETHI in the framework of the ESA's campaign, TropiSAR.

This paper deals with the characterization of permanent scatterers in polarimetric synthetic aperture radar (SAR) images of urban environment. To this aim, the main purpose of this paper is to investigate how spaceborne SAR tomography (TomoSAR) can be employed to identify and distinguish the target scattering mechanisms. Along this, the conventional H-α classifier can be adapted to the reconstructed polarimetric coherence matrix, i.e. T, in a multi-dimensional space. However, dealing with multitemporal multi-baseline satellite images, the accurate tomographic reconstruction requires permanent scatterers between all the acquisitions. To cope with this issue, a generalized likelihood ratio test (GLRT)-based tomographic approach for polarimetric SAR tomography is developed. The proposed framework of scatterer detection and characterization is evaluated using TerraSAR-X polarimetric multi-baseline data sets over an urban area in France.

Earth observation using spaceborne sensors with short revisiting time has forgone the limits of conventional assessment methods. In this paper, we investigate the use of SAR Tomography (TomoSAR) to monitor infrastructures, using Morandi Bridge in Genoa, Italy, as a case study. Morandi Bridge suffered a partial collapse due to a structural failure on August 14, 2018. The main issues to be considered for using TomoSAR as a means of an early warning system in structural health monitoring are discussed, highlighting the differences respect to monitoring applications dealing with subsidence and deformation of extended areas. A set of 70 Sentinel-1A images of the bridge over a period of three years prior to the bridge collapse are analyzed using 5D TomoSAR based on Sup-GLRT scatterers detection technique. Preliminary results indicate that possible temporal and thermal deformations of the static structure can be estimated using the method considered.

In this paper, the potential of a deep learning approach for SAR tomography (TomoSAR) is investigated. TomoSAR is a powerful technique that allows the 3D reconstruction of objects lying on the Earth surface, by separating multiple scatterers with different elevations laying in the same range-azimuth resolution cell. In urban applications, the number of interfering scatterers is typically very small, so that the reconstruction of the elevation reflectivity profile can be faced as a statistical detection problem. Detection performance depends on how well the adopted statistical model fits to the observed scene. For complex urban scenarios this issue can greatly impair achievable accuracy of results. Then, we propose to exploit the neural networks' capabilities to learn the data generative model, in order to face the problem of signal model inaccuracies. In particular, in the assumption of a single scatterer, a neural network can be trained to solve a simple classification problem. Results on simulated and real data are presented.

This paper explores the main issue surrounding the multidimensional synthetic aperture radar (SAR) image focusing techniques, suchlike those caused by the atmosphere propagation delays or by residual platform motion. The problem brings unknown contributions to the phases of complex received signal that it is generally independent of acquisitions track to track and leads spreading and defocusing in multi-dimensional space. To deal with these issues, in this paper an auto-focusing procedure based on sharpness optimization of the reconstructed signal has been employed. The main concern about this technique is that sharpness optimization by itself however can introduce unwanted and uncontrollable vertical shifts in the focused image. To tackle this issue, the phase error is estimated by multiple integration of second derivative of the phase with respect to baseline. The estimation of the calibration phase is performed by optimizing contrast or entropy of the vertical profile with the constraint of a zero phase derivative. In this way, unwanted vertical shifts are avoided and the correct height reference is preserved. Experimental results from the proposed method are evaluated by vertical profile reconstruction performance in the controlled conditions by simulated dataset over the forested area and multi-baseline data acquired by ONERA in Guyana in the frame of European space agency's campaign TROPISAR.

Synthetic aperture radar (SAR) tomography is the most typical approach to generate the elevation map of the observed scene through the 3D imaging from multi-baseline acquisition. Typically, the nominal scatterers can be derived by evaluation of the presented peaks in the reconstructed scattering reflectivity through the array signal processing methods. In this paper, we investigate the possibility to improve the height reconstruction process and achieve a reliable elevation map by jointly estimating and regularizing the solution of array processing techniques. In particular, an a priori is added to the cost function of desired parameter estimation in the processing chain. The algorithm is evaluated using simulated data correspond to the E-SAR airborne sensor of the German Aerospace Center (DLR).

This paper addresses the problem of SAR Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers in presence of partially correlated Gaussian clutter. TomoSAR is a multidimensional imaging technique that has proven its ability in localizing the scatterers, reconstructing the elevation profile of the structures on the ground (3D reconstruction) and estimating the temporal deformations and thermal dilations of the scene (5D reconstruction). In the literature statistical based TomoSAR reconstruction refers to a signal model where in each range-azimuth resolution cell one or more scatterers are interfering in presence of noise and clutter signals, modeled as zero-mean complex circular white Gaussian random vectors. In this paper, we propose to extend a generalized likelihood ratio test (GLRT) detector, proposed by the authors and denoted Fast-Sup-GLRT, to a different signal model, where a correlated clutter model is considered. Results on TerraSAR-X real data are presented.

SAR tomography (TomoSAR) allows facing the problem related to the interference of coherent scatterers within the same pixels due to the occurrence of layover. Whereas, full imaging the continuous reflectivity profile along the elevation dimension is a typical framework to deal with the non-coherent volumetric scatterers in the forested area. Layover usually arises in volumetric scenario and leads to discontinuity in the reconstructed vertical reflectivity image. This paper aims to investigate the possibility of addressing layover issue in forested area by exploiting the unified non-local (NL) filtering of multi-baseline (MB) covariance matrix. To this aim, the performance of non-parametric Capon spectral estimation technique has been analyzed using the NL filtered MB covariance matrix and efficient vertical reflectivity profile reconstruction is demonstrated, which almost addressed the layover issues.

In the last decade, Persistent Scatterer Interferometry (PSI) and SAR tomography (TomoSAR) have been used for reconstructing the elevation profile of a scene, starting from a set of co-registered Synthetic Aperture Radar (SAR) images. The possible advantage of TomoSAR over classical interferometric methods consists in the potential capability of improving the detection of single scatterers presenting stable proprieties over time (Persistent Scatterers or PS), as well as to enable the detection of multiple scatterers interfering within the same range-azimuth resolution cell. In urban environment, when only single dominant scatterers are present in each range-azimuth resolution cell, both methods can be exploited to estimate the altitude, deformation rate and thermal expansion of a subset of reliable scatterers, which are selected on the basis of different criteria. This paper is focused on a performance analysis of the two class of methods, using the results obtained in urban environment on simulated and real TerraSAR-X data. A concise description of both techniques, along with a discussion on their potential capabilities in selecting the most reliable scatterers, is given.

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.

Detection of multiple scatterers for localizing the targets is one of the key issues in SAR tomography. Recently, a Generalized Likelihood Ratio Test based on support estimation (Sup-GLRT) [10] has been presented. This test exhibits a high computational complexity. In this paper a modified approach for reducing computational complexity (Fast-Sup-GLRT) is proposed. The prime objective is to analyze the performance of Fast-Sup-GLRT detector in terms of implementation and computational complexity. For an assigned probability of false alarm and with a given number of acquisitions the performance is analyzed and compared with the one obtained with the Sup-GLRT. Results on simulated and real HighRes SpotLight TerraSAR-X data are presented.

In this paper we extend the Fast-Sup-GLRT Detector, designed for SAR tomography (3D-SAR), to the detection of multiple scatterers that can exhibit time deformation. It assumes at most Kmaxdifferent scatterers in the same range-azimuth resolution cell with a phase model that takes into account phase variations due to the deformation and/or dilation of the scatterer(s). Results on simulated and real data are presented to validate the proposed approach.

In this paper we focus on the detection of single and double scatterers in SAR tomography. In particular, the performance of a support based Generalized Likelihood Ratio Test (GLRT) approach is analyzed, using TerraSAR-X system parameters, with particular reference to the elevation resolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Results on simulated and real data are presented.

In this paper we investigate SAR image compression based on sparse representation. Two approaches are considered: the first one is based on the use of an Overcomplete ICA transform coding method, the second one is based on Compressive Sensing (CS). In both cases an Overcomplete ICA representation is used as sparse representation, but while in the first case the significant overcomplete ICA coefficients are coded using an optimal entropy constrained threshold quantizer, in the latter case a reduced number of measurements obtained combining the SAR image pixels through a random measurement matrix are directly coded. Numerical results on TerraSAR-X images are presented.

In this paper, the detection of single persistent scatterers, exploiting multi-pass interferometric SAR acquisitions, is addressed. A Generalized Likelihood Ratio Test (GLRT) approach that takes into account the presence of correlated clutter is proposed and compared with the case in which clutter is assumed to be white. To assess the performance of the proposed approach, detection results obtained on simulated and real data are presented.

The availability of high-resolution along-track interferometric synthetic aperture radar (ATI-SAR) data with large coverage, such as TerraSAR-X (TSX) data, motivates spaceborne ground moving target detection as an attractive alternative to conventional traffic data acquisition. In this paper, a performance analysis of ground moving targets detection by means of ATI-SAR systems and using a statistical approach is carried out on both simulated and real data. A Gaussian clutter model and a deterministic target response have been assumed. The receiver operating characteristic for the likelihood ratio test (LRT), which can be assumed as a reference best performance case, has been expressed in closed form and has been related to the deflection values, which can be exploited for assessing the improvements in the detection probability with a constant false-alarm rate. For practical applications, the performance of a generalized LRT (GLRT) has been investigated. The analysis carried out on simulated data revealed that the detection results achieved using a GLRT based on a deterministic target model are comparable with those obtained using a GLRT based on a Gaussian target model and are not significantly worse than the theoretical performance of the LRT. Finally, ground moving target detection results on TSX real data are showed.

Synthetic Aperture Radar (SAR) systems provide images with a resolution related to the transmitted signal and Doppler bandwidths. High resolution systems require large bandwidths, and then high sampling rates. Processing techniques based on Compressive Sensing (CS) can be applied for reducing sampling frequency and/or increasing spatial resolution. They are based on the assumption of a sparse reflectivity map of the imaged scene. The achievable performance depends on the degree of sparsity and on the level of noise affecting processed data. In this paper these issues are investigated by means of numerical experiments on simulated raw data for realistic SAR images.

In this paper we investigate the application of a GLRT detector of multiple scatterers in SAR tomography, exploiting the sparsity assumption of the signal in the elevation direction. The GLRT approach aims at estimating the signal support (positions of the samples different from zero of the unknown sparse signal) that best matches to the data for detecting and positioning multiple scatterers lying in the same range-azimuth resolution cell.

SAR tomography allows the three-dimensional (3D) reconstruction of the reflectivity profile of the observed scene on the ground. It is based on the acquisition of several images of the same scene, collected with different view angles along slightly different orbits of the SAR platform. The spatial resolution in the elevation direction obtained with conventional Fourier-type techniques is related to the overall baseline extent of the acquisition orbits. Recently a tomographic technique denoted as Compressive Sampling Tomography (CST) has been introduced. It allows a drastic reduction of the number of acquisitions required and attains an increased resolution in the elevation direction. It exploits the sparsity property of the reflectivity profile in the elevation direction. In this paper some results obtained by applying CST to COSMO-Skymed real data are presented.

In this paper we assess the ground moving target detection performance based on a GLRT approach, using ATI SAR data and considering two different models for the target signal: deterministic and Gaussian model. We will show also some preliminary results on real TerraSAR-X data.

In this paper we investigate the problem of detecting single coherent scatterers in multidimensional (elevation-velocity) SAR imaging. We exploit both phase and amplitude information and a GLRT approach. Multipass/multiview SAR data are used and performances are assessed referring to two different models for the scatterers: deterministic and Gaussian model.

Along-track interferometric synthetic aperture radar systems can be used for ground moving target indication. We analyze a scheme for detecting moving targets with unknown parameters (velocity and signal-to-clutter ratio) and with constant false-alarm rates, based on the generalized likelihood ratio test (GLRT), and adopting a Gaussian model for target and clutter signals. We compare its performance with the one obtained in the ideal case of known target parameters applying the likelihood ratio test (LRT). A closed form for the LRT receiver operating characteristic is derived and used as reference for GLRT performance assessment. The analysis is carried out on simulated TerraSAR-X data.

Recently Synthetic Aperture Radar Along Track Interferometric (ATI-SAR) has been successfully applied for traffic monitoring. In this work we treat the problem of detecting moving targets with unknown parameters (velocity and signal to clutter power ratio) from ATI-SAR images, exploiting a Generalized Likelihood Ratio Test (GLRT). We provide a closed form for the probability density function (pdf) of the log-likelihood ratio, for the threshold corresponding to an assigned probability of false alarm (PFA) and for the Receiver Operating Characteristic (ROC). The moving target detection capabilities are investigated on TerraSAR-X data provided by DLR.

Recently, a 3-D SAR imaging technique based on Compressive Sampling (CS) has been introduced . It allows to noticeably decrease the number of acquisitions required and to obtained an increased elevation resolution. The number of acquisitions required to obtain a reliable 3-D reconstruction is related to the desired resolution and to the overall elevation extension of the scene. If the number of measurements is not sufficiently high, artifacts can appear in the reconstruction. In this paper, this issue is investigated on COSMO-Skymed simulated data.

Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.

In this paper a comparison between two techniques developed to recover layover solution in SAR images is presented. SAR Statistical Tomography and Compressive Sensing techniques are described and analyzed in order to provide a set of instruments for 3D SAR imaging able to tackle different scattering mechanisms in layover areas and to recover height reconstruction of an observed scene. The performances of the two techniques are compared on simulated data and some conclusions are drawn.

In this paper we analyze the performance of ground moving target detection by means of single-baseline and dual-baseline along track interferometric synthetic aperture radar (SAR) systems, obtained using a generalized likelihood ratio test (GRLT). Detection performance are evaluated in terms of probability of detection and probability of false alarm using data simulated with TerraSAR-X parameters.

In this paper a 3-D SAR imaging technique based on Compressive Sampling is experimented on ERS 1-2 data. The technique is based on the sparsity property of the image to be focused along the elevation direction (i.e. only few scatterers with different elevation are present in the same range-azimuth resolution cell), exploits a reduced number of unevenly spaced acquisitions and allows an increased elevation resolution. Numerical results on real data are compared with those obtained by using Truncated Singular Value Decomposition (TSVD) techniques.

In this paper a methodology for the reconstruction of height profile of earth surface starting from layover affected Synthetic Apertuire Radar data is presented. The proposed approach is based on classical statistical estimation techniques, in particular using Maximum Likelihood Estimator, together with a Gaussian model for the point target response. Multi-channel configuration has been exploited in order to solve the solution ambiguity and to increase the reconstruction accuracy. The performances of the proposed estimator have been evaluated in comparison with the Cramer Rao Lower Bounds for the considered model, showing the effectiveness of the method. The height reconstruction procedure has been tested on a simulated realistic scenario, providing interesting and promising results.

In this paper we consider the problem of the detection of a ground moving target using Synthetic Aperture Radar Along Track Interferometric (AT- InSAR) systems. We propose a method exploiting a Generalized Likelihood Ratio Test (GRLT) and based on a Gaussian model for the target response. We also derive the log-likelihood ratio probability density function in closed form, both in the hypothesis of presence of target and absence of target. Numerical results based on simulated data are presented.

Three dimensional (3-D) Synthetic Aperture Radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. The practical application of the focusing methods requires that non-uniformly spaced acquisition orbits have to be considered. In this paper we propose a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell. Numerical results on simulated data show the good performance of the method.

In this letter, a statistical-based approach to recover layover solution in synthetic aperture radar (SAR) images is proposed. The aim of this letter is to develop a methodology in order to separate different scattering contributions collapsed in a single SAR image pixel. After a brief discussion about layover, the proposed model is presented, followed by a discussion about achievable performances using Cramer-Rao lower bounds. In the final part of this letter, the performances of a maximum likelihood estimator are evaluated in a simulated data scenario, showing the effectiveness of the method.

We show that using the in-phase and quadrature components of the two acquired images in AT-InSAR systems produces an increasing of the detection probability of ground moving targets for constant false alarm rates respect to the detection performance obtained with AT-InSAR conventional systems using only phase information. In this paper, we consider a Gaussian model for the moving target response. The improved performances of the proposed method respect to the interferometric phase approach are showed with numerical experiments on simulated data, and varying the signal to clutter ratio (SCR) and the target radial velocity.

Along-track interferometric synthetic aperture radar (AT-InSAR) can be used to estimate the radial velocity of ground moving targets, starting from interferometric phase measures. The estimation obtained from a single-phase interferogram suffers from ambiguities. To solve these problems, multichannel AT-InSAR systems are required. In this letter, we analyze the radial velocity maximum-likelihood estimation accuracy with respect to AT-InSAR system parameters, such as velocity values and different clutter and thermal noise levels. We consider two different models for the target response: a deterministic model and a zero-mean Gaussian model. The presented results show that AT-InSAR systems exhibit better estimation accuracies for low-velocity values (slow targets).

Along-Track Interferometric SAR (AT-InSAR) can be used to measure moving ground target velocity. In this paper we have investigated how, exploiting multi-channel interferograms, the velocity estimation accuracy depends on the statistical model assumed for the interferometric phase. We have verified the different accuracies in the estimation of the target velocity by investigating the changes in the Cramer-Rao bounds with respect to two different target image statistical model, a deterministic and a Gaussian model. Generative and estimation model have been tested with numerical experiments on simulated data.

In this paper the application of a transform coding technique, based on overcomplete independent component analysis (ICA), for the compression of single look intensity synthetic aperture radar (SAR) images is explored. The method has the advantage of representing the image through almost statistically independent coefficients, with an assigned distribution, so that a scalar entropy constrained quantizer, optimized for the coefficients statistics, can be used. Numerical results on ERS-1 data are presented.

In this paper we introduce an algorithm for velocity estimation of a ground moving point target using a multi-channel along-track interferometry (MC-ATI) system. The presented results are relative to a multi frequency system, but the algorithm can be used also for a multi-baseline one. The performance of the system is evaluated by presenting also the probabilities of detection.

One of the most effective ways of modeling vector data for unsupervised pattern classification or coding, is to assume that the observations are the result of picking randomly out of a fixed set of different distributions. In this paper we propose to perform the unsupervised estimation of the mixture density underlying the data as the problem of separating multiclass sources. Assuming in each class independent components, standard linear independent component analysis (ICA) can be adopted in the recently extended mode which provides signal reconstruction for a multiclass mixture. Unfortunately, in practical problems the class densities necessary to match the experimental distributions must be degenerate or poorly conditioned. In this paper we approach the problem by assuming from the beginning sources which have either rank-deficient distributions or show very concentrated eigenvalues. The class membership of each point is based on a distance measure from the hyperplanes and on the likelihood on each hyperplane. The independent components are then searched within each subspace. We present results of the algorithm on synthetic distributions with various degrees of degeneracy. Our results are promising for feature extraction applications.