With the increase of high spatial and temporal resolution SAR data availability, novel applications and information extraction techniques become possible. Among these, the extraction of micro-motion information has the potential to unlock a range of applications, such as infrastructure monitoring, maritime surveillance and natural disaster damage assessment. However, sensors, acquisition modes and products have not been designed with in mind the optimization of micro-motion extraction and its applications, therefore, careful considerations need to take place when selecting the most suitable data and designing processing algorithms. In this paper practical and processing considerations when dealing micro-motion extraction from high-resolution SAR sensors are discussed and supported with experimental results obtained from Capella, Umbra and TerraSAR-X data.

Phase unwrapping is a key problem in several technical fields, among which SAR interferometry (InSAR). In this work, a new computationally efficient approach to spatio-temporal three-dimensional (3D) phase unwrapping is proposed. The method makes it feasible to process very large datasets fully exploiting the information from the spatial and temporal structures of the data. The global 3D phase unwrapping problem is separated into smaller subproblems, each of them global spatially or temporally, which are solved iteratively till convergence to a suboptimal but still very good solution. Moreover, to further improve computational efficiency, we propose an iterative least-squares strategy for phase unwrapping that exploits the integer character of the phase ambiguities to be recovered and the repeated structures in the subproblems to be solved. As a third element, which can additionally improve computational performance and result robustness, a pyramidal solution strategy can be considered, consisting of successive phase unwrapping of subsets of points, typically with increasing noise, each constrained by the previous results. The tests performed on simulated and real satellite InSAR data confirm the validity of the proposed approach.

This work consolidates the existing results in the field of information extraction from spaceborne SAR imagery based on Inverse Synthetic Aperture Radar (ISAR) techniques, as well as enhances the understanding of the phenomenology, the models, the processing algorithms, the applications, and the overall value in security applications. The focus is on ISAR based advanced processing methods to explore the potentialities of very high resolution (VHR) SAR data in a range of security related application domains, including maritime, inland water, and land scenarios.

Synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor from satellite very large areas and detect motions of the ground surface (typically due to subsidence, landslides, earthquakes, and volcanic phenomena) with millimetric precision and sub-metric spatial detail (making it possible to distinguish different parts of buildings or infrastructures). A key step of this technology is the identification of points (or clusters of points) providing a coherent backscattering over time. These points typically correspond to man-made structures, rocks, or bare soil, and can be called persistent scatterers (PSs) regardless of whether the dominant diffusion mechanism is point-like or distributed. In this work, we propose a novel algorithm, which we will call point coherence estimation (PCE), to evaluate in a clean and simple way (without the need for articulated procedures and critical assumptions or approximations) the interferometric coherence of each single point in an interferometric image series. The method exploits the well-accepted assumptions that large phase artefacts such as atmospheric delays or orbital effects are almost identical between points within tens or hundreds of meters, whereas phase noise (e.g., thermal noise and time, spectral, geometric decorrelation noises) is relatively small w.r.t. a phase cycle and has statistically independent realizations in different points (possibly excluding adjacent pixels if the images are oversampled). Based only on these assumptions, it is possible to write an overdetermined system of linear equations to evaluate the coherence of each single point reliably and consistently from the coherences of pairs of points, which can be calculated directly. The tests performed on simulated and real datasets confirm the validity and great potential of the method.

This paper presents a novel processing chain based on Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) techniques to refocus SAR images of moving maritime targets and estimate their motion parameters. The proposed processing chain was developed and extensively evaluated The algorithm was tested on a large dataset of COSMO-SkyMed (CSK) and Cosmo Second Generation (CGS) dataset including 300 vessels, and spanning different maritime scenarios, in order to account for many contingencies such as the sea states, the ship movements, and the mutual geometry between the SAR orbit and the course of ship. To assess the refocusing capability, quantitative contrast measurements of the refocused images were conducted. The accuracy of vessel speed estimation was evaluated by comparing the results with Automatic Identification System (AIS) data as a reference. The key contribution of this work lies in demonstrating the effectiveness of ISAR processing applied to satellite SAR images for near real-time Maritime Awareness applications. This was achieved through the development of a robust and fully automatic processing chain, as well as an extensive experimentation and validation process.

Satellite interferometric SAR (InSAR) has demonstrated to be a powerful technology to perform millimeter-scale precision measurements of ground motions typically caused by landslides, subsidence, earthquakes or volcanic activity, and to monitor the stability of slopes, mining areas, buildings, infrastructures, etc. This work presents the European Ground Motion Service (EGMS), funded by the European Commission as an essential element of the Copernicus Land Monitoring Service (CLMS). EGMS constitutes the first application of the interferometric SAR (InSAR) technology to high-resolution monitoring of ground deformations over an entire continent, based on full-resolution processing of all Sentinel-1 (S1) satellite images over most of Europe. EGMS employs advanced persistent scatterer (PS) and distributed scatterer (DS) InSAR processing techniques. Moreover, a global navigation satellite system (GNSS) model is realized to calibrate the InSAR ground motion products. To foster as wide usage as possible, EGMS also provide tools for visualization, exploration, analysis and download of the ground deformation products, as well as elements to promote best practice applications and user uptake.

Ship detection using synthetic aperture radar images is a key technology in maritime surveillance applications. In addition to the position of the vessel, the characterization of the target (length, width and orientation) is often a requirement. In this paper, we present a deep learning architecture for object detection we developed by modifying the popular YOLOv3 architecture to apply to vessel detection and parameter estimation from SAR images. The proposed architecture was trained and tested on a large dataset of SAR images defined in this work. It contains images covering a wide range of spatial resolutions (pixel spacing ranging from 1.5m to 50m) and labelled with oriented bounding boxes to associate to each vessel not only its position but also size and orientation. The obtained results are very promising and confirm the validity of the approach.

The current remote sensing (RS) open data policy for multispectral (MS) missions such as Sentinel-2 and Landsat-8, together with the availability of free cloud distributed processing platforms such as Google Earth Engine, makes it possible the quick generation of burned area (BA) products even for nonexperts in the field. Indeed, fires and BAs can be detected using burn severity indices, which are usually obtained by simple band algebra operations. However, simple approaches can aid BA estimation only if typical error patterns are known and accounted for, especially when working at large (e.g., continental) scales. This article proposes an automatic BA detection system based on burn severity index thresholding, which integrates dedicated false and missed alarm mitigation strategies to improve the detection accuracy. The system is tested on Sentinel-2 and Landsat-8 data over ten different locations in Europe and spanning year 2018. Three known burn severity indices plus a custom one defined to improve the performance in the considered study area are under study. Results show that burned index thresholding is possible within accuracy bounds slightly larger than the state of the art, which is acceptable by considering the proposed simplified processing framework.

The increasing availability of huge amounts of satellite data, together with the increasing computation power available at relatively low cost, is requiring and at the same time allowing the development of new algorithms to automatically extract information from the data. In this work, we propose a new method for automatic detection, from series of satellite images, of possible anomalies relative to crop parcels declared by farmers in the framework of EU's Common Agricultural Policy (CAP). Differently from other recently explored methods, our technique is not based on a crop classification approach. On the contrary, we approached the problem as an anomaly detection problem, and our method bases only on the quite realistic and general assumption that declarations are mostly correct, with a moderate number of outliers. Therefore, our technique is robust to variations in weather conditions, terrain morphology and agriculture practices. In order to detect the anomalies, the method computes the “distances” between the different parcels with a given declared crop. In particular, the time series of the features extracted from the satellite data on the different parcels are compared. Their distance is defined according to the Dynamic Time Warping (DTW) method, robust to temporal variations. The tests performed were very good, and the technique has been already operationally used with satisfactory results. In particular, the automatic anomaly detection approach has made it possible to verify all the farmers' declarations in a large area (a significant portion of Italy), and will make it possible to process even larger areas, such as the whole Italy or the whole Europe.

Ship detection SAR images is a key technology in maritime surveillance applications. We show here the main issues arising in the processing SAR data for ship detection and how techniques, based on Constant False Alarm Rate (CFAR) algorithms, have evolved to face them and to achieve operational performances. This evolution followed the advancements in the satellite SAR system technology that have increased acquisition capability in terms of always greater number of images and modes, better spatial resolutions and wider swaths. We describe an operational CFAR ship detection algorithm having some novel features with respect to CFAR algorithms available in literature to improve quality, robustness and processing time.

Interferometric processing of a time series of acquisitions from synthetic aperture radar (SAR) satellites makes it possible to detect and measure ground motion phenomena, typically caused by landslides, subsidence, earthquakes or volcanic activity, with millimeter-scale precision. This enables, for example, monitoring of the stability of slopes, mining areas, buildings and infrastructures. This work presents the European Ground Motion Service (EGMS), funded by the European Commission as an essential element of the Copernicus Land Monitoring Service (CLMS). The EGMS constitutes the first application of the interferometric SAR (InSAR) technology to high-resolution monitoring of ground deformations over an entire continent, based on full-resolution processing of all Sentinel-1 (S1) satellite acquisitions over most of Europe (Copernicus Participating States). Upscaling from existing national precursor services to pan-European scale is challenging. The EGMS will employ the most advanced persistent scatterer (PS) and distributed scatterer (DS) InSAR processing techniques in combination with a high-quality Global Navigation Satellite System (GNSS) model to calibrate the ground motion products. To foster as wide usage as possible, the EGMS will also provide tools for visualization, exploration, analysis and download of the ground deformation products, as well as elements to promote best practice applications and user uptake.

Oil spills, caused by accidents or by ships cleaning their tanks, represent big threats for maritime and coastal ecosystems health. A very effective detection of oil spills can be performed using satellite synthetic aperture radar (SAR) systems, operating regardless of cloud coverage and sunlight and capable of discriminating oil from regular sea surface. However, discriminating between real oil spills and lookalikes (such as natural oils and seepages, often occurring in upwelling sea areas), although well performed by expert SAR image interpreters, poses a great challenge for automatic processes. In addition, a visual check performed by human operators on a great number of images would be too expensive. Therefore, many solutions for automatic detection have been tried in the last few years, using probabilistic models and, more recently, machine learning. This work presents an innovative solution based on image-to-image translation using convolutional neural networks (CNNs) trained with an adversarial loss function. The proposed approach has been tested, with very promising results, using Radarsat-2 and Sentinel-1 SAR data over the Mediterranean Sea and some areas of the Atlantic Ocean and the North Sea.

Remote sensing present some new challenges for deep learning, because (also to compensate the scarce detail level) multimodal, multisource and multitemporal data should be jointly exploited. For example, time series of optical multispectral/hyperspectral or synthetic aperture radar (SAR) data probe different properties of the observed scene, based on their different wavelength, acquisition geometry, etc., and with possible data gaps. To address this task, we propose a new deep learning architecture that exploits a sequence of deep convolutional neural networks (CNN) and a recurrent neural network (RNN). In the proposed architecture, all the data (with their spectral, spatial and temporal information) are used jointly and optimally in the sense that no imputation is enforced, but the internal weights providing the best classification results are estimated from the data themselves (hence the proposed name ODIN - Optimal Data Imputation Network). We have tested the proposed architecture, using Sentinel SAR and multispectral image series, on land cover and crop classification, an important remote sensing application. The obtained results are very promising, with an error rate below 1%, and show good spatial consistency without loss of spatial resolution.

Persistent scatterer Interferometry (PSI) is a technique that processes a stack of satellite synthetic aperture radar (SAR) images and measures displacement and 3D position, with millimetric and metric precision respectively in correspondence of sparse points named persistent scatterers (PS). Very dense measurements (e.g., up to tens of thousands of PS per square kilometer with high resolution COSMo-SkyMed SAR data) can be obtained in correspondence of each satellite acquisition date, and over each single infrastructure. This wealth of information is extremely meaningful, but at the same time requires expert knowledge to be converted in a concise description of the underlying phenomena. In this paper, we present a technique that can support end user decisions through automatic information extraction from the PS measurements. The proposed approach is based on: precise location of measurements trough integration with 3D models of the objects under investigation; reconstruction of a model of the displacement integrating measurement from different observation geometries; extraction of statistical indices related to the stability of the infrastructure. The proposed approach appears promising as demonstrated from the results achieved on real COSMO-SkyMed data.

Synthetic aperture radar (SAR) interferometry is a powerful and consolidated technology to detect and monitor slow ground surface movements. However, information extraction and interpretation from big sets of InSAR deformation measurement is still a complex and demanding task. In this paper, we present a new approach for automatic analysis and detection deformations anomalous in the spatial or the temporal dimension. The method takes in input InSAR deformation measurements. In the spatial dimension, in order to reduce the dataset size, we utilize a hierarchical clustering method to obtain convergence points which are more trustful, and then to detect spatially inhomogeneous deformations. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the PS velocity variation in each identified homogeneous period is analyzed to detect anomalous velocity trends and accelerations. We have tested the method on different cases, and we report the results obtained on PS measurements from high-resolution X-band COSMO-SkyMed data on some sites in China. The proposed method appears very promising, as confirmed from the in-field survey, in enabling the detection of deformation anomalies that could cause building or infrastructure stability problems, and in some cases could bring to collapses and potential disasters.

Image coregistration aims at stacking two or multiple images in a way such that, for each image, the same pixel corresponds to the same point of the target scene (possibly with sub-pixel accuracy). We can distinguish two families of image coregistration problems, basically depending on if the images to be coregistered are taken by sensors of the same or different type (e.g., sensing different wavelenghts), and with similar or different illumination and acquisition geometries (e.g. different sun illumination conditions and/or different acquisition incidence angles). Whilst the first type of image coregistration is well established, multimodal coregistration is not yet well founded and due to difficulty of finding correspondences between the images (tie points) in a robust way, and the avable approaches often recur to manual assistance. The multimodal image coregistration technique proposed in this work overcomes the problems due to differences in radiometries and in geometries by exploiting two main concepts: complementary geometry information between the images to be coregistered, and mutual information (or entropy) as similarity metric. The method focuses on coregistration of very high resolution synthetic aperture radar (SAR) and optical images, but the approach is of general validity. The tests performed on real very high resolution optical and SAR data confirm the validity of the method.

Recently, different very high-resolution synthetic aperture radar (SAR) missions have been launched, but the great potential of SAR systems for intelligence and defense purposes has been only partially exploited until now, because SAR images are much more difficult to interpret by human operators w.r.t. optical ones. In particular, the aspect of targets in SAR images depends dramatically on the relative orientation between line of sight and target. We have devised a methodology and we have developed a prototype for automatic target recognition on SAR imagery. Differently from the few previous studies available in the literature, our approach is based on machine learning techniques applied to the images themselves, possibly after some linear or nonlinear filterings to improve robustness. A SAR simulator and CAD models to create a database of SAR target signatures for the classifier training. The developed prototype was validated on an extended set of images of military vehicles taken by different SAR satellite and airborne systems, with good results confirming the validity of the proposed approach.

The availability of several sensors with complementarity characteristics, in terms of spatial resolution and tasking flexibility, offers new opportunities for SAR interferometry applications. In this work, we quantitatively discuss the complementarity of C-band low-resolution sensors (Sentinel-1 and Envisat) and the X-band high-resolution COSMO-SkyMed SAR constellation, both with theoretical analyses and experiments, focusing the comparison on interferometric measurements on real targets, in terms of density, deformation precision, and 3D positioning precision. Moreover, we provide a characterization of the measurement precision as a function of the interferometric baseline values. The obtained results confirm that whereas Sentinel-1 can systematically cover very large areas, COSMO-SkyMed can guarantee a more detailed analysis, which can be fundamental for building and infrastructure monitoring.

The contribution focuses on the current status of the ESA study entitled “InSARAP Sentinel-1 Constellation Study”, which investigates the interferometric performance of the S1A/S1B units. General aspects like the interferometric compatibility in terms of common range and Doppler bandwidth and the burst synchronization are addressed. Besides the first interferometric results with both units, time series results over the pilot sites combining both satellites are also shown, as well as some investigations with fast moving (i.e., glaciers) scenarios.

Synthetic aperture radar (SAR) interferometry is a powerful technology for detection and monitoring of slow ground surface movements. Extraction of this information is a complex task. The persistent scatterer pair (PSP) approach was recently proposed to overcome some limitations of standard persistent scatter interferometry. The PSP method exploits only the relative properties of neighboring points to avoid problems caused by atmosphere and in general artefacts slowly variable in space. In this work, after resuming the main ideas of the PSP method, we describe the PSP measurements obtained from high-resolution X-band COSMO-SkyMed data over Wuhan, China. Moreover, we validate these results by comparison with optical leveling measurements, geological studies, and field surveys. The outcomes confirm the validity of the PSP method and demonstrate that very accurate ground deformation and building stability measurements can be obtained from COSMO-SkyMed data.

The availability of long time series of interferometric data acquired all over the world from several synthetic aperture radar (SAR) satellite missions makes possible to perform a worldwide analysis of ground surface deformations and infrastructure stability by SAR interferometry. When this technology is applied to large areas, several problems have to be faced to handle huge amounts of data. In this work, we present the first example in the world of persistent scatterer (PS) SAR interferometry analysis at national scale (the whole Italian territory), performed with ERS, Envisat and COSMO-SkyMed SAR data acquired from 1992 till 2014. Moreover, we discuss the complementarity of high resolution SAR systems like COSMO-SkyMed, with the new Sentinel-1 SAR satellite, which has lower resolution but larger swath. Based on these characteristics, we explore the possible worldwide extension of our national experience.

This paper presents results of the Sentinel-1 sensor in the interferometric wide-swath (IW) mode encompassing the first two years of operation of the mission. The paper focuses on persistent scatterer interferometric results and their validation. Further applications and investigations are also addressed, e.g., earthquakes, volcanoes and tomography.

The availability of long time series of interferometric data acquired all over the world from several synthetic aperture radar (SAR) satellite missions makes possible to perform a worldwide assessment of the terrain and infrastructure stability by persistent scatterer (PS) SAR interferometry techniques. This technology is computationally demanding, in particular because it requires a 3D processing. When applied to large areas, several problems have to be faced to handle huge amounts of data. In this work, we present a significant example of PS big data processing performed at national scale (the whole Italian territory) with ERS, Envisat and COSMO-SkyMed data. The main challenges and results related to this project are discussed, and the possible worldwide extension with Sentinel data is suggested.

Since the launch of the first COSMO-SkyMed satellite back in 2007, e-GEOS has always been in first line in the analysis of VHR SAR data and development of new applications. In this paper we will present some of the latest activities in the Earth Observation domain, with special focus on the use of COSMO-SkyMed data for real operational services, for interferometric-based services and for some innovative applications in new fields.

This contribution reports on the performance investigations of the interferometric wide swath (IW) mode of Sentinel-1, which is implemented using the terrain observation by progressive scans (TOPS) mode. The key aspects of the TOPS mode that need to be considered for accurate interferometric processing will be presented, and first analyses with Sentinel-1 time series will be shown. The results focus on the pilot sites of Campi Flegrei/Vesuvius and Mexico City, as well as Greenland glaciers. Other aspects related to the interferometric performance are also presented, like the burst synchronization, the pointing accuracy, or the considerations when evaluating non-stationary scenes.

Due to the finite pulse repetition frequency and non-ideal antenna pattern, the presence of “ghosts” on SAR images of maritime scenes is frequently observed. This phenomenon can lead to an increase in false alarm rate in ship-detection applications. In this paper we propose to use the recently developed “asymmetric mapping and selective filtering” (AM&SF) method for the filtering of azimuth ambiguities on stripmap SAR images as a preliminary step of an adaptive-threshold cell-averaging constant-false-alarm-rate ship-detection algorithm. We show that use of this preliminary filtering step allows us to significantly improve the performance of the ship detection by reducing the false alarm rate, without reducing the detection rate. The proposed framework is positively applied to a couple of Cosmo/SkyMed SAR images.

The present work focuses on the analysis of post-seismic surface deformation detected in the region of L'Aquila, Central Italy, after the strong earthquake that hit the city and the surrounding villages on April 6, 2009. The analysis has been carried out thanks to a new dataset of SAR COSMO-SkyMed images, and to the adoption of the Persistent Scatterer Pairs (PSP) approach. This method allow the estimate of surface deformations by exploiting the SAR images at full resolution. Two patterns of subsidence have been identified reaching a maximum value of 45 mm in the northeast area of the L'Aquila town. Here the subsidence is mainly ascribable to the post seismic slip release of the Paganica fault and it does not coincide with the maximum measured coseismic subsidence. The time series of the ground deformations also reveal that a large amount of deformation is released in the first three months after the main shock. The second pattern of deformation interests the Mt. Ocre ridge, where a detailed photogeological analysis allowed us to identify widespread evidence of morphological elements associated with Deep-seated gravitational slope deformation (DGSD). In this sector the observed deformation is mainly ascribable to a gravitative cause.

Synthetic aperture radar (SAR) interferometry is an effective technology for detection and monitoring of slow terrain movements with millimetric precision. This information is extracted by means of complex techniques from the phase of the signal. Building on the ideas of the persistent scatterer pair (PSP) method, we present an enhanced method aimed at fully extracting the coherent information even from low intensity SAR signals. The proposed method is very effective at extracting the available information from each single pixel of the SAR image where you could expect a coherent signal, even when there are not strongly scattering structures and the sensed signal is weak, as in the case of rather smooth surfaces or natural terrains. Several examples obtained from the processing COSMO-SkyMed data show that unprecedentedly dense ground deformation measurements can be obtained with the enhanced PSP method, not only corresponding to structures but also in natural terrains.

In the last few years high and very high resolution SAR data have become available, opening new possibilities in the field of SAR interferometry. However, the huge amount of data poses new challenges in terms of computational and memory requirements, in particular to those processing steps that require a global approach to obtain good results, such as elevation or velocity finite difference integration and phase unwrapping. In this paper we propose two approaches to overcome this problem. In the first approach the data to be processed are divided in blocks of smaller size, and different strategies are suggested to make the solutions of the different blocks consistent. The second approach is based on a decomposition of the problem at different scales according to a pyramidal scheme, which makes possible to exploit the available information at a global level (thus guaranteeing optimal quality results) with scalable computational demand. The proposed approaches were successfully tested on high resolution COSMO-SkyMed full frame data.

Phase unwrapping and integration of finite differences are key problems in several technical fields, among which is synthetic aperture radar (SAR) interferometry. In this paper, we propose a general formulation for robust and efficient integration of finite differences and for phase unwrapping, which includes standard techniques (e.g., minimum cost flow and least squares phase unwrapping) as subcases. The proposed approach allows obtaining more reliable and accurate solutions by exploiting redundant differential estimates (not only between nearest neighboring points) and multidimensional information (e.g., multitemporal). In addition, a model of the signal (e.g., multibaseline or multifrequency) or external data (e.g., GPS or leveling measurements) can be integrated. The method requires the solution of linear or quadratic programming problems, for which computationally efficient algorithms exist. The validation tests performed on real and simulated SAR data confirm the validity of the method, which was integrated in our production chain and successfully used also in massive productions.

Phase unwrapping and integration of finite differences are key problems in several technical fields, among which SAR interferometry. In this paper we propose a general formulation for robust and efficient integration of finite differences and for phase unwrapping, which includes standard techniques methods as sub-cases. The proposed approach allows obtaining more reliable and accurate solutions by exploiting redundant differential estimates (not only between nearest neighboring points) and multi-dimensional information (e.g. multitemporal, multi-frequency, multi-baseline), or external data (e.g. GPS measurements). The method requires the solution of linear or quadratic programming problems, for which computationally efficient algorithms exist. The validation tests performed confirm the validity of the technique.

Synthetic aperture radar (SAR) interferometry is an effective technology for detection and monitoring of slow terrain movements with millimetric resolution. This information is extracted by means of complex techniques from the phase of the signal, which is wrapped modulo 2¿ and affected by noise and systematic terms. We have recently proposed a new method, named persistent scatterer pairs (PSP), aimed at overcoming some limitations of standard persistent scatter interferometry (PSI) techniques. The method is characterized in that it works only with pairs of nearby pixels both for selecting and analyzing the persistent scatterers (PS), thus being intrinsically not affected by artifacts slowly variable in space, like those depending on atmosphere or orbits. Moreover, the method does not require an initial selection of PS based on the radar backscattered amplitude. In this work, after resuming the main ideas of the PSP method, we show some results obtained in extensive applications with ERS/ENVISAT data, and the first results obtained with high resolution COSMO-SkyMed images.

Synthetic aperture radar (SAR) interferometry is a powerful technology for measuring slow terrain movements. The extraction of this information is a complex task, because the phase of the signal is measured only modulo 2pi and is affected by noise and systematic terms. The persistent scatterer (PS) approach brought important advances in the solution of this problem. In this work, we present a new method, named persistent scatterer pairs (PSP) method, for the identification and the analysis of PS in series of full resolution SAR images. The problems coming from orbital and atmosphere phase artifacts are effectively overcome by exploiting their spatial correlation, without using model based interpolations or fits, which can be advantageous when the atmospheric artifacts or the displacement to be retrieved are not very well described by the models used in the standard PS approach. Moreover, the proposed method does not need a preprocessing to calibrate the data and is insensitive to the density of PS candidates, it is able to identify PS in natural terrains and PS characterized by non linear movements, is computationally efficient and highly parallelizable. The results obtained on real ERS SAR data confirm the validity of the proposed approach.

Multi-Dimensional (MultiD) SAR imaging is a modern technique, based on coherent SAR data combination, aimed to space (full-3D) and space deformation-velocity (4D) analysis. It extends the concept of SAR interferometry and differential interferometry and offers new options for the analysis and monitoring of ground scenes. With this regard, we discuss the current status and the results obtained by processing ERS real data, we investigate perspectives related to the next generation multi-static satellite formations, and we show some sample results regarding 3D and 4D theoretical performance bounds.

Differential synthetic aperture radar (SAR) interferometry allows measuring slow terrain movements. The extraction of this information is a complex task. Important advances were introduced by the persistent scatterer approach, with the ideas of minimizing the amplitude and phase dispersions in long series of SAR acquisitions. This approach exploits mainly the temporal properties of the signals. On the contrary, other approaches, more similar to classical differential interferometry, exploit first the spatial and then the temporal properties of the data. In this work, we present a generalized formulation of the persistent scatterer interferometry problem that contains the two approaches mentioned above as limiting cases. In the general case, the spatial and temporal properties of the data are exploited jointly, which helps recovering the correct solution even with a limited number of images. Tests performed on real ERS data show that the proposed approach is promising.

Given a series of SAR acquisitions, when a sufficient number of differential interferograms between different dates are computed and the phases unwrapped, the phases of each possible time interval can be obtained through a linear combination of the computed ones, i.e. by the solution of a determined linear system of equations. Usually (e.g. with ERS data), not all the interferograms necessary to obtain a determined system can be computed, unless one accepts that only few pixels (corresponding to stable point-like scatterers) remain coherent. In fact, spatial and temporal baselines can be very large. Previous works proposed to solve the under-determination of this system by singular value decomposition, i.e., by assuming that the solution (i.e. the terrain displacement) has minimum velocity. In this work, a different assumption is exploited in order to find a determined solution to the problem of combining SAR multitemporal differential interferometric measurements. The proposed approach is based on the idea that the solution should have minimum curvature. Tests performed on simulated and ERS SAR real data confirm the validity of the method.

Baseline calibration is a needed step in all applications of SAR interferometry and differential interferometry. A new approach for baseline calibration is proposed, based on the idea of maximizing the correlation between the original complex interferogram and reference values of it obtained from ground control points. The main advantage with respect to traditional techniques is that the method does not require the phase to be unwrapped in advance, and therefore the results are not affected by possible unwrap errors. In addition, successive phase unwrap is facilitated by the better phase flattening possible after baseline calibration. The method is computationally more demanding than traditional techniques, though the requested computational time is comparable with that of other processing steps of SAR interferometry. Tests performed on real ERS SAR images confirm the validity of the proposed approach.

In this paper we explore the use of differential synthetic aperture radar interferometry to improve our knowledge of the slope instability of a well investigated area (the Maratea Valley) affected by continuous slow movements. In particular, by using this technique we analyse the time evolution of terrain movements from 1997 to 2000, a time interval already explored using distancemeter (EDM) and GPS measurements. Results obtained by means of different techniques have been compared, and all the acquired data turn out to be consistent.

Phase unwrapping is the problem of reconstructing a function on a grid given its values modulo 2/spl pi/. This is a key problem in SAR interferometry and in other fields. The typical availability of multiple 2D SAR interferograms of the same scene suggest the possibility of considering the data as samples of a function in a 3D space-time. This helps better reconstructing the right solution, in the same way as 2D phase unwrapping provides more reliable solutions than the 1D (quite trivial) algorithm. However, computational needs result increased in the 3D case. In this work we describe the proposed algorithm for 3D phase unwrapping, and show the results obtained on simulated and real SAR images.

Image segmentation is the problem of finding the homogeneous regions (segments) in an image. Applications of image segmentation range from filtering of noisy images to problems of feature extraction and recognition. In this work we present a novel approach for image segmentation problems. The proposed technique is based on the idea of splitting the original image segmentation problem in two subproblems with lower computational complexity. First, a preliminary estimate of the segmented image gradient is found by solving a number of one-dimensional segmentation problems. In a second step, the results are merged together by enforcing that the obtained vector field is irrotational. At the cost of obtaining a "sub-optimal" solution, the computational advantage coming from the proposed decomposition can allow the implementation of sophisticated strategies that would be practically impossible to implement in a unique step. The results obtained on real and simulated image confirm the validity of the proposed approach.

Phase unwrapping is the key problem in building the elevation map of a scene from interferometric synthetic aperture radar (SAR) system data. Phase unwrapping consists in the reconstruction of the phase difference of the radiation received by two SAR systems as a function of the azimuth and slant range coordinates. The data available to reconstruct the phase difference are a measure of the difference module 2/spl pi/. The authors propose a phase unwrapping method that makes use of the equivalent, in a discrete space, of the irrotational property of a gradient vector field. This property is used first to locate the areas where the discrete vector field estimated from the available data must be corrected, and then, with the knowledge of some a priori information, to perform the correction needed to obtain a useful estimate of the discrete gradient of the phase difference function, from which the phase difference function is reconstructed. The use of the fast Fourier transform makes it possible to have a fast algorithm, that is to process an image of N pixel in O(NlogN) elementary operations. Tests of the method proposed here on real and simulated data are presented.

Phase unwrapping is the reconstruction of a function on a grid given its values mod 2/spl pi/. Phase unwrapping is a key problem in all quantitative applications of synthetic aperture radar (SAR) interferometry, but also in other fields. A new phase unwrapping method, which is a different approach from existing techniques, is described and tested. The method starts from the fact that the phase differences of neighboring pixels can be estimated with a potential error that is an integer multiple of 2/spl pi/. This suggests the formulation of the phase unwrapping problem as a global minimization problem with integer variables. Recognizing the network structure underlying the problem makes for an efficient solution. In fact, it is possible to equate the phase unwrapping problem to the problem of finding the minimum cost flow on a network, for the solution of which there exist very efficient techniques. The tests performed on real and simulated interferometric SAR data confirm the validity of the approach.