Diego Reale

Four-Dimensional SAR Imaging for Height Estimation and Monitoring of Single and Double Scatterers

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

Tomographic Imaging and Monitoring of Buildings With Very High Resolution SAR Data

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

Bridge Thermal Dilation Monitoring With Millimeter Sensitivity via Multidimensional SAR Imaging

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

CAESAR: An Approach Based on Covariance Matrix Decomposition to Improve Multibaseline–Multitemporal Interferometric SAR Processing

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

Tomographic Processing of Interferometric SAR Data: Developments, applications, and future research perspectives

Synthetic aperture radar (SAR) data processed with interferometric techniques are widely used today for environmental risk monitoring and security. SAR tomography techniques are a recent advance that provide improved three-dimensional (3-D) reconstruction and long-term deformation monitoring capabilities. This article is meant to discuss the main developments achieved in the last few years in the SAR tomography framework, with particular reference to both urban and forest scenarios. An insight on classical multipass interferometric processing is also included to summarize the importance of the technology for natural hazards monitoring and to provide the basis for the description of SAR tomography.

Extension of 4-D SAR Imaging to the Monitoring of Thermally Dilating Scatterers

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

A Null-Space Method for the Phase Unwrapping of Multitemporal SAR Interferometric Stacks

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

Multilook SAR Tomography for 3-D Reconstruction and Monitoring of Single Structures Applied to COSMO-SKYMED Data

With reference to the application to the imaging and monitoring of infrastructures and buildings in urban areas, SAR tomography has been mainly developed and tested at full resolution. In this work, we investigate the possibility related to the use of a multilook approach for fine resolution analysis of ground structures that combines SAR tomography and a method, CAESAR, recently proposed for classical DInSAR analysis at coarse resolution over large areas. Shown results, achieved by processing two 3 m spatial resolution (stripmap mode) COSMO-SKYMED datasets relative to the urban areas of Naples and Rome (Italy), clearly indicate that the proposed multilook-based method allows achieving an impressive density of detected scatterers over buildings and infrastructures, much higher than those achievable with standard full-resolution methods.

4-D SAR Imaging: The Case Study of Rome

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

Edge Detection Using Real and Imaginary Decomposition of SAR Data

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