Single-Look SAR Tomography of Urban Areas

Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-ofsight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed

Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems

Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne or on a space-borne vehicle. The relative motion between the targets on ground and the platform causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves. In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically, thus allowing even a radar discrimination capability in the range direction (i.e. in distance). For side-looking acquisition geometries, the along-track and the range directions are almost orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to produce images of the illuminated area on ground. A side-looking geometry consists in the radar antenna to be, either mechanically or electronically, oriented perpendicular to the observed area. Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack and range directions in the order of few tenths of centimeters. Since the SAR is a microwave active sensor, this technology assure the possibility to produce images of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR a very useful instrument for monitoring and mapping both the natural and the artificial activities over the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them which are briefly listed below: a) the performance achievable in terms of resolution are usually paid in terms of system complexity, dimension, mass and cost; b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally radio-frequency interferences which might limit normal system operability; c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from moving targets. These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system

Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems

Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne or on a space-borne vehicle. The relative motion between the targets on ground and the platform causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves. In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically, thus allowing even a radar discrimination capability in the range direction (i.e. in distance). For side-looking acquisition geometries, the along-track and the range directions are almost orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to produce images of the illuminated area on ground. A side-looking geometry consists in the radar antenna to be, either mechanically or electronically, oriented perpendicular to the observed area. Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack and range directions in the order of few tenths of centimeters. Since the SAR is a microwave active sensor, this technology assure the possibility to produce images of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR a very useful instrument for monitoring and mapping both the natural and the artificial activities over the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them which are briefly listed below: a) the performance achievable in terms of resolution are usually paid in terms of system complexity, dimension, mass and cost; b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally radio-frequency interferences which might limit normal system operability; c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from moving targets. These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Estimation of the Minimum Number of Tracks for SAR Tomography

SAR Tomography (SARTom) is the natural extension of SAR Interferometry (InSAR) to solve for multiple phase centers within a resolution cell and obtain the 3-dimensional (3D) representation of a scene. This paper deals with the determination of the minimum number of tracks required to perform SARTom. Through the prolate spheroidal wave functions, the number of equivalent targets of a volumetric source is derived and from it, the minimum number of observations required to apply subspace super-resolution methods is computed. The minimum tomographic aperture length is also investigated. The results are validated on real data acquired in L-band by the E-SAR system of the German Aerospace Center

Theoretical Performance Bounds on the Estimation of Forest Structure Parameters From Multibaseline SAR Data

Given their central role in the carbon budget, the SAR remote sensing of forests has become during the last two decades a “hot” research topic. A powerful way to analyze forest scattering consists in the coherent combination of multibaseline (MB) SAR data, possibly also polarimetric. For instance, SAR Tomography is a powerful technique whose natural output is the 3-D imaging in the range-azimuth-height space, thus allowing the resolution of multiple scatterers in height in the same cell. As a consequence, the extraction of a high amount of information is made possible, e.g. forest height and biomass, radar reflectivities, sub-canopy topography, soil humidity, volume extinction [1]. In the last years, many tomographic algorithms have been conceived for the estimation of forest structure parameters in both parametric and non parametric frameworks and their performance have been judged against the available in-situ measurements [2-5]. However, not so many efforts have been spent in the analytical derivation of theoretical performance bounds, despite their primary importance. In fact, such tools provide a benchmark against which it is possible to compare the performance of any estimator. Not only, but they alert to the physical impossibility of finding an estimator whose performance is lower than the bounds. This work offers a contribution to tackle the performance bounding problem by resorting to the Cramér-Rao bound (CRB) theory. The CRB is a result of the information theory which provides a lower bound on the variance of any unbiased estimator of an unknown parameter. Given also its relative easiness of calculation, the CRB is widely used in the statistical signal processing to judge the efficiency of the parameter estimators. In the specific MB SAR field, it could also be a very useful instrument to characterize the potentials of acquisition configurations and possibly as a guideline in designing acquisition patterns (mission planning) and systems. An interesting extension of the CRB is represented by the Hybrid CRB (HCRB), in which the presence of random phase offsets between different acquisitions (e.g. due to non perfect baseline estimation and/or propagation effects through the atmosphere) can be taken into account. In particular, in this work the CRB and HCRB derivations are focused to the analysis of forest areas by assuming a two-layer model for the MB data vector, i.e. a ground layer and a canopy layer, with different characteristics of their vertical structure. Starting from the very general formulations of MB bounds in [6] and [7], ready-to-use CRB and HCRB formulas are given for forest scenarios. Moreover, the obtained precision limits on the parameters of interest are calculated numerically for some realistic acquisition patterns and for different observed scenarios. The presence of temporal decorrelation is considered in the model, which is recognized to be one of the main application barriers of MB repeat-pass forest observations, especially from space [8]. References [1] A. Reigber, A.Moreira, “First Demonstration of Airborne SAR Tomography Using Multibaseline L-Band Data,” IEEE Trans. on Geoscience and Remote Sensing, vol. 38, 2000. [2] F. Lombardini, M. Pardini, “Experiments of Tomography-Based SAR Techniques with P-Band Polarimetric Data”, Proc. of the 2009 ESA PolInSAR Workshop. [3] M. Nannini, R. Scheiber, et al., “Estimation of the Minimum Number of Tracks for SAR Tomography,” IEEE Trans. on Geoscience and Remote Sensing, vol. 47, 2009. [4] M. Neumann, L. Ferro-Famil, et al., “Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric Data,” IEEE Trans. on Geoscience and Remote Sensing, vol. 48, 2010. [5] S. Tebaldini, “Single and Multipolarimetric SAR Tomography of Forested Areas: A Parametric Approach,” IEEE Trans. on Geoscience and Remote Sensing, vol. 48, 2010. [6] F. Gini, F. Lombardini, M. Montanari, “Layover Solution in Multibaseline SAR Interferometry,” IEEE Trans. on Aerospace and Electronic Systems, vol. 38, 2002. [7] M. Pardini, F. Lombardini, F. Gini, “The Hybrid Cramér-Rao Bound for Broadside DOA Estimation of Extended Sources in Presence of Array Errors,” IEEE Trans. on Signal Processing, vol. 56, pp. 1726–1730, Apr 2008. [8] F. Lombardini, F. Cai, M. Pardini, “Parametric Differential SAR Tomography of decorrelating Volume Scatterers,” Proc. of the 2009 European Radar Conference (EURAD)

Enhanced TomoSAR Imaging through Statistical Regularization

Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful remote sensing tool that allows the retrieval of a 3D representation of the illuminated scene [1] - [4]. A set of images, acquired with a different line-of-sight (LOS), is combined coherently using SAR interferometric techniques. Later on, the power spectrum pattern (PSP), in the direction perpendicular to the LOS (PLOS), is recovered using spectral analysis (SA)-based methods. The TomoSAR problem at hand is treated as an ill-conditioned nonlinear inverse problem [5], [6], and is commonly tackled within the direction-of-arrival (DOA) estimation framework [2] - [6]. The DOA-inspired non-parametric techniques, as the conventional matched spatial filter (MSF) and minimum variance distortionless response (MVDR) beamformers [1] - [4], are well suited to cope with distributed targets, since these techniques recover an estimate of the continuous power spectrum pattern (PSP); nonetheless, the achievable resolution highly depends on the span of the tomographic aperture. Alternatively, super-resolved parametric approaches, as multiple signal classification (MUSIC) [3], [4], have the main drawback related to the white noise model assumption that guaranties the separation of the signal and noise sub-spaces. On the other hand, taking advantage of the sparse representations of the cross-range tomographic profiles in the wavelet domain, super-resolved compressed sensing (CS)-based approaches [7], [8], are also employed to solve the TomoSAR inverse problem. However, CS-based techniques often imply a considerable computational burden, due to their iterative nature and due to the non-availability of adapted efficient convex optimization algorithms. To overcome such drawbacks and as an alternative to the aforementioned commonly performed TomoSAR-adapted focusing techniques, statistical regularization approaches can be applied instead, in the context of the statistical decision-making theory. Assuming no a priori knowledge about the statistical distribution of the desired PSP, to be retrieved, and imposing no constrain on linearity, the Bayes minimum risk (BMR) methodology is extended to the maximum-likelihood (ML) approach [5], [6]. Then, to guarantee well-conditioned solutions (in the Hadamard sense) to the TomoSAR nonlinear inverse problem, the derived ML-based approach is implemented in a closed fixed-point iterative adaptive manner, yielding the so-called MARIA (ML-inspired Adaptive Robust Iterative Approach) technique [5]. The use of statistical regularization approaches, within the maximum likelihood (ML) estimation theory, to solve the involved TomoSAR nonlinear ill-conditioned inverse problem, has been successfully demonstrated in the previous related studies [5], [6]. Within the main advantages of such approaches there is the retrieval of resolution-enhanced tomograms using a reduced (limited) number of passes, performing also suppression of artifacts and reduction of the ambiguity levels. Once the theoretical background of statistical regularization was provided, and its use for enhanced TomoSAR imaging was demonstrated, the subject of the work to be presented is focused on its application on different test sites and on the cross-check analysis of the retrieved measurements. [1] A. Reigber and A. Moreira, “First demonstration of airborne SAR tomography using multibaseline L-band data”, IEEE Trans. Geosc. Remote Sens., vol. 38, no. 5, pp. 2142–2152, Sep. 2000. [2] F. Gini, F. Lombardini and M. Montanari, “Layover solution in multibaseline SAR interferometry”, IEEE Trans. Aerosp. Electron. Syst., vol. 38, no.4, pp. 1344-1356, Oct. 2002. [3] M. Nannini, R. Scheiber, and A. Moreira, “Estimation of the minimum number of tracks for SAR tomography”, IEEE Trans. Geosc. Remote Sens., vol. 47, no. 2, pp. 531-543, Jan. 2009. [4] M. Nannini, R. Scheiber, R. Horn, and A. Moreira, “First 3-D reconstructions of targets hidden beneath foliage by means of polarimetric SAR tomography”, IEEE Geoscience and Remote Sensing Letters, vol. 9, no.1, pp. 60-64, Jan. 2012. [5] G. D. Martín del Campo, M. Nannini, and A. Reigber, “Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach”, IEEE Geoscience and Remote Sensing Letters, pp. 1–5, August 2018. [6] G. Martín del Campo, A. Reigber and M. Nannini, “Feature Enhanced SAR Tomography Reconstruction through Adaptive Nonparametric Array Processing”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018. [7] E. Aguilera, M. Nannini and A. Reigber, “A Data-Adaptive Compressed Sensing Approach to Polarimetric SAR Tomography of Forested Areas”, IEEE Geoscience and Remote Sensing Letters, vol. 10, no.3, pp. 543–547, Sept. 2012. [8] E. Aguilera, M. Nannini and A. Reigber, “Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas”, IEEE Trans. Geosc. Remote Sens., vol. 51, no.12, pp. 5283–5295, Dec. 2013