Antonio Pauciullo

Detection of Single Scatterers in Multidimensional SAR Imaging

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space deformation-velocity (4-D) analysis. It is an extension of the concepts of SAR interferometry and differential interferometry SAR and offers new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting single scatterers for localization and monitoring issues. To this end, we resort to a constant false alarm rate (CFAR) detection scheme which can be synthesized according to three different design criteria: generalized likelihood ratio test, Rao test, and Wald test. At the analysis stage, the performance of the aforementioned detector is compared to that of a previously proposed CFAR scheme, based on the multi-interferogram complex coherence and widely used in persistent scatterer interferometry. The analysis is conducted both on simulated and on real SAR data, acquired by ERS-1/2 satellites. Finally, Cramer-Rao lower bounds for the estimation of the scatterer elevation and velocity are provided.

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.

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.

Phase difference-based multichannel phase unwrapping

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

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.

LMMSE 3-D SAR Focusing

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

Phase Offset Calculation for Airborne InSAR DEM Generation Without Corner Reflectors

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