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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach

One of the main objectives of the upcoming space missions, such as Tandem-L and BIOMASS, is to map, on a global scale, the forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR). On one hand, the number of baselines is constrained to the revisit time that avoids temporal decorrelation issues. On the other hand, enhanced resolution is desired, since the forest structure is characterized from the vegetation layers that compose it, reflected in the tomographic profiles as local maxima. The TomoSAR nonlinear ill-conditioned inverse problem is conventionally tackled within the direction-of-arrival (DOA) estimation framework. The DOA-inspired nonparametric techniques are well suited to cope with distributed targets; nonetheless, the achievable resolution highly depends on the span of the tomographic aperture. Alternatively, superresolved parametric approaches have the main drawback related to the white noise model assumption that guaranties the separation of the signal and noise subspaces. Overcoming the disadvantages of the aforementioned techniques, in this letter, we address a novel maximum-likelihood (ML) inspired adaptive robust iterative approach (MARIA) for feature-enhanced TomoSAR reconstruction. MARIA performs resolution enhancement, with suppression of artifacts and ambiguity levels reduction, to an initial estimate of the continuous power spectrum pattern. After convergence, an accurate location of the closely spaced phase centers is achieved, easing the characterization of the forest structure. The feature-enhancing capabilities of the proposed approach are corroborated using airborne F-SAR data of the German Aerospace Center (DLR)

Forest Analysis Using SAR Tomography and Maximum Likelihood Inspired Spectral Estimation

This paper treats the synthetic aperture radar (SAR) tomography (TomoSAR) non-linear inverse problem, within the framework of maximum likelihood (ML) estimation theory. In this context, a novel non-parametric spectral analysis (SA) technique, addressed in a closed fixed-point iterative fashion, is presented. The main goal of the proposed approach is to provide resolution-enhancement, with suppression of artifacts and ambiguity levels re-duction, to an initial estimate of the continuous power spectrum pattern (PSP), retrieved using the celebrated matched spatial filter (MSF) beamforming technique. The feature enhancing capabilities of the proposed method are corroborated via processing L-band airborne multi-baseline SAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the forested test site of Froschham, Germany, in 2017

Enhanced-resolution SAR tomography using the weighted covariance fitting criterion

The use of maximum likelihood (ML)-inspired statistical regularization approaches to solve the synthetic aperture radar (SAR) tomography (TomoSAR) nonlinear ill-conditioned inverse problem, has been successfully demonstrated in previous related studies. Within the main advantages of these approaches there is the retrieval of resolution-enhanced tomograms using a reduced number of passes, performing also suppression of artifacts and reduction of the ambiguity levels. Nonetheless, such techniques are constrained to the probability density function (pdf) Gaussian assumption of the observed data. Consequently, in order to ease this restriction and preserving the previously mentioned desired char-acteristics, in this paper, we relax such Gaussian assumption and apply the weighted covariance fitting (WCF) criterion instead. The feature enhancing capabilities of the proposed WCF-based technique are corroborated via processing L-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the for-ested test site of Froschham, Germany (2017) and L-band airborne TomoSAR data of the Jet Propulsion Laboratory (JPL) from the National Aeronautics and Space Administration (NASA), in collaboration with DLR, acquired by the UAVSAR system over the urban area of München, Germany (2015)

Subsurface TomoSAR imaging of the Mittelbergferner glacier in the Austrian Alps

In February 2019, as part of the Enceladus explorer (EnEx) project of the German Aerospace Center (DLR), an F-SAR campaign was organized along the Mittelbergferner glacier in the Austrian Alps. One of the goals of this campaign is to analyze the ice-crust through synthetic aperture radar (SAR) tomography (TomoSAR). During the TomoSAR data acquisitions, different experiments were conducted in situ by the University of Wuppertal, in order to retrieve permittivity measurements at different depths. Making use of this information, the aim of this work is to study the capabilities of TomoSAR to detect the subsurface structures in such an environment. The TomoSAR data is to be focused using the super-resolved statistical regularization technique called WISE, which stands for weighted covariance fitting (WCF)-based iterative spectral estimator. Furthermore, we perform multi-looking using the non-local spatially adaptive Beltrami filter, which enhance the estimation of the data covariance matrices, improving the scatterer separation in layover areas thanks to its smoothing and edge-preserving properties

Statistical Regularization as an Alternative to Model Order Selection

The correct functioning of parametric focusing techniques [e.g., MUltiple SIgnal Classification (MUSIC)] require a proper selection of the model order. For such aim, a methodology based on the Kullback-Leibler information criterion is commonly employed. These methods perform well due to its propensity to choose relatively large model orders, which tend to retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit. However, some solutions can be misleading, since only the most proper model order (i.e., the actual number of targets) guaranties best performance. As an alternative, this work suggests employing statistical regularization instead of model order selection (MOS) approaches. First, a model with large order is chosen to perform focusing via parametric methods; subsequently, statistical regularization is applied, seeking to attain good-fitted solutions. To demonstrate the capabilities of the addressed novel strategy, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is considered as application

Parameter Selection Criteria for TomoSAR Focusing

The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direc-tion-of-arrival estimation theory. The latter allows achieving super-resolution, along with ambiguity levels reduction, thanks to the use of parametric focusing methods, as multiple signal classification (MUSIC), and statistical regularization techniques, like the maximum-likelihood inspired adaptive robust iterative approach (MARIA). Nevertheless, in order to correctly suit the considered signal model, MUSIC and most regularization ap-proaches require an appropriate setting of the involved parame-ters. In both cases, the accuracy of the retrieved solutions de-pends on the right selection of the assigned values. Thus, with the aim of dealing with such an issue, this article addresses sev-eral parameter selection strategies, adapted specifically to the TomoSAR scenario. Parametric techniques as MUSIC solve the TomoSAR problem in a different manner as the regularization methods do, hence, each approach demands different methodol-ogies for the proper estimation of their parameters. Conse-quently, we refer to the Kullback-Leibler information criterion for the model order selection of parametric techniques as MUSIC, whereas we rather explore the Morozov’s discrepancy principle, the L-Curve, the Stein’s unbiased risk estimate and the generalized cross-validation, to choose the regularization pa-rameters. After the incorporation of these criteria to MUSIC and MARIA, respectively, their capabilities are first analyzed through simulations, and later on, utilizing real data acquired from an urban area

SURE-Based Regularization Parameter Selection for TomoSAR Imaging via Maximum-Likelihood

Regularized iterative reconstruction algorithms for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), like the ones based on Maximum Likelihood (ML), offer an accurate estimate of the Power Spectrum Pattern (PSP) displaced along the Perpendicular to the Line-of-Sight (PLOS) direction. The recovered PSP is considered as ‘good-fitted’ or ‘appropriate-fitted’, since the reconstruction fits correctly enough with the position and density of the objectives in the field backscattered towards the sensor. However, the correct functioning of these regularization approaches is constrained to the proper selection of the regularization parameters. Therefore, for such a purpose, this paper suggests using a criterion based on the Stein’s Unbiased Risk Estimate (SURE) strategy. SURE approximates the Mean Square Error (MSE) between the estimated and actual PSP, purely from the measured (observed) data, without the need of any knowledge about the true PSP. Consequently, the proper selection of the regularization parameters corresponds to the minimum SURE value, which guarantees having a ‘good-fitted’ reconstruction. The experiments are performed in simulated data for different representative cases

ESTIMATION OF STRUCTURED COVARIANCE MATRICES FOR TOMOSAR FOCUSING

Most common focusing techniques for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), e.g. Matched Spatial Filtering and Capon, make use of the conventional sample covariance matrix, obtained from a finite number of observations. Yet, structured covariance matrix estimates can be employed in lieu of the sample covariance matrix. Accordingly, our simulation study shows that Capon's performance improves with the use of structured covariance matrices. These are obtained with the Subspace Fitting approach, properly adapted to TomoSAR. Numerical comparisons between structure and unstructured covariance matrices are presented