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.

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.

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.

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.

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.

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.

This paper presents a novel method for sensor-based exploration of unknown environments by a general robotic system equipped with multiple sensors. The method is based on the incremental generation of a configuration-space data structure called Sensor-based Exploration Tree (SET). The expansion of the SET is driven by information at the world level, where the perception process takes place. In particular, the frontiers of the explored region efficiently guide the search for informative view configurations. Different exploration strategies may be obtained by instantiating the general SET method with different sampling techniques. Two such strategies are presented and compared by simulations in non-trivial 2D and 3D worlds. A completeness analysis of SET is given in the paper.

We present a method for sensor-based exploration of unknown environments by a robotic system equipped with rangefinders. The method is based on the incremental generation of a configuration-space data structure called sensor-based exploration tree (SET). The expansion of the SET is driven by information at the world level, where the perception process takes place. In particular, the frontiers of the explored region are used to guide the search for informative view configurations. Various exploration strategies may be obtained by instantiating the general SET method with different sampling techniques. Two of these are compared by simulations in 2D and 3D worlds.