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

Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction

GNSS and InSAR based water vapor tomography: A Compressive Sensing solution

An accurate knowledge of the three-dimensional (3D) distribution of water vapor in the atmosphere is a key element for weather forecasting and climate research. In addition, a precise determination of water vapor is also required for accurate positioning and deformation monitoring using Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). Several approaches for 3D tomographic water vapor reconstruction from GNSS-based Slant Wet Delay (SWD) estimates exist. Yet, due to the usually sparsely distributed GNSS sites and due to the limited number of visible GNSS satellites, the tomographic system usually is ill-posed and needs to be regularized, e.g. by means of geometric constraints that risk to over-smooth the tomographic refractivity estimates. Therefore, this work develops and analyzes a Compressive Sensing (CS) approach for neutrospheric water vapor tomographies benefiting of the sparsity of the refractivity estimates in an appropriate transform domain as a prior for regularization. The CS solution is developed because it does not include any geometric smoothing constraints as applied in common Least Squares (LSQ) approaches and because the sparse CS solution containing only a few non-zero coefficients may be determined, at a constant number of observations, based on less parameters than the corresponding LSQ solution. In addition to the developed CS solution, this work introduces SWDs obtained from both GNSS and InSAR into the tomographic system in order to dispose of a better spatial distribution of the observations. The novelties of this approach are 1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and 2) the solution of the tomographic system by means of Compressive Sensing. In addition, 3) the quality of the CS reconstruction is compared with the quality of common LSQ approaches to water vapor tomography. The tomographic reconstruction is performed, on the one hand, based on a real data set using GNSS and InSAR SWDs and, on the other hand, based on three different synthetic SWD data sets generated using wet refractivity information from the Weather Research and Forecasting (WRF) model. Thus, the validation of the achieved results focuses, on the one hand, on radiosonde profiles and, on the other hand, on a comparison of the refractivity estimates with the input WRF refractivities. The real data set resp. the first synthetic data set compares the reconstruction quality of the developed CS approach with LSQ approaches to water vapor tomography and investigates in how far the inclusion of InSAR resp. synthetic InSAR SWDs increases the accuracy and precision of the refractivity estimates. The second synthetic data set is designed in order to analyze the general effect of the observing geometry on the quality of the refractivity estimates. The third synthetic data set places a special focus on the sensibility of the tomographic reconstruction to different numbers of GNSS sites, varying voxel discretization, and different orbit constellations. In case of the real data set, for both the GNSS only solution and a combined GNSS and InSAR solution, the refractivities estimated by means of the LSQ and CS methodologies show a consistent behavior, although the two solution strategies differ. The synthetic data sets show that CS can yield very precise and accurate results, if an appropriate tomographic setting is chosen. The reconstruction quality mainly depends on i) the accuracy of the functional model relating the SWD estimates to the refractivity parameters and to the distances passed by the rays within the voxels, ii) the number of available GNSS sites, iii) the voxel discretization, and iv) the variety of ray directions introduced into the tomographic system. The sizes of the study areas associated to the real resp. to the synthetic data sets are about 120 × 120 km2 and about 100 × 100 km2, respectively. In the real data set, a total of eight GNSS sites is available and SWD estimates of GPS and InSAR are introduced. In the synthetic data sets, different numbers of sites are defined and a variety of ray directions is tested

Ground-based synthetic aperture radar (GBSAR) interferometry for deformation monitoring

Ph. D ThesisGround-based synthetic aperture radar (GBSAR), together with interferometry, represents a powerful tool for deformation monitoring. GBSAR has inherent flexibility, allowing data to be collected with adjustable temporal resolutions through either continuous or discontinuous mode. The goal of this research is to develop a framework to effectively utilise GBSAR for deformation monitoring in both modes, with the emphasis on accuracy, robustness, and real-time capability. To achieve this goal, advanced Interferometric SAR (InSAR) processing algorithms have been proposed to address existing issues in conventional interferometry for GBSAR deformation monitoring. The proposed interferometric algorithms include a new non-local method for the accurate estimation of coherence and interferometric phase, a new approach to selecting coherent pixels with the aim of maximising the density of selected pixels and optimizing the reliability of time series analysis, and a rigorous model for the correction of atmospheric and repositioning errors. On the basis of these algorithms, two complete interferometric processing chains have been developed: one for continuous and the other for discontinuous GBSAR deformation monitoring. The continuous chain is able to process infinite incoming images in real time and extract the evolution of surface movements through temporally coherent pixels. The discontinuous chain integrates additional automatic coregistration of images and correction of repositioning errors between different campaigns. Successful deformation monitoring applications have been completed, including three continuous (a dune, a bridge, and a coastal cliff) and one discontinuous (a hillside), which have demonstrated the feasibility and effectiveness of the presented algorithms and chains for high-accuracy GBSAR interferometric measurement. Significant deformation signals were detected from the three continuous applications and no deformation from the discontinuous. The achieved results are justified quantitatively via a defined precision indicator for the time series estimation and validated qualitatively via a priori knowledge of these observing sites.China Scholarship Council (CSC), Newcastle Universit

GNSS and InSAR based water vapor tomography: A Compressive Sensing solution

Unvollständig oder ungenau erstellte Modelle atmosphärischer Effekte schränken die Qualität geodätischer Weltraumverfahren wie GNSS (Globale Satelliten-Navigationssysteme) und InSAR (Interferometrisches Radar mit synthetischer Apertur) ein. Gleichzeitig enthalten Zustandsgrößen der Erdatmosphäre, allen voran die dreidimensionale (3D) Wasserdampf-Verteilung, wertvolle Informationen für Klimaforschung und Wettervorhersage, welche aus GNSS- oder InSAR-Beobachtungen abgeleitet werden können. Es gibt etliche Verfahren zur 3DWasserdampf-Rekonstruktion aus GNSS-basierten feuchten Laufzeitverzögerungen. Aufgrund der meist spärlich verteilten GNSS-Stationen und durch die begrenzte Anzahl sichtbarer GNSS-Satelliten, treten in tomographischen Anwendungen in der Regel jedoch schlecht gestellte Probleme auf, die z.B. über geometrische Zusatzbedingungen regularisiert werden, welche oft glättend auf die Wasserdampf-Schätzungen wirken. Diese Arbeit entwickelt und analysiert daher einen Ansatz, der auf einer Compressive Sensing (CS) Lösung des tomographischen Modells beruht. Dieser Ansatz nutzt die Spärlichkeit der Wasserdampf-Verteilung in einem geeigneten Transformationsbereich zur Regularisierung des schlecht gestellten tomographischen Problems und kommt somit ohne glättende geometrische Zusatzbedingungen aus. Eine weitere Motivation für die Nutzung einer spärlichen Compressive Sensing Lösung besteht darin, dass die Anzahl an zu bestimmenden von Null verschiedenen Koeffizienten bei gleichbleibender Anzahl an Beobachtungen in Compressive Sensing geringer sein kann als die Anzahl an zu schätzenden Parametern in üblichen Kleinste Quadrate (LSQ) Ansätzen. Zur Erhöhung der räumlichen Auflösung der Beobachtungen führt diese Arbeit zudem sowohl feuchte Laufzeitverzögerungen aus GNSS als auch aus InSAR in das tomographische Gleichungssystem ein. Die Neuheiten des vorgestellten Ansatzes sind 1) die Nutzung von sowohl GNSS als auch absoluten InSAR Laufzeitverzögerungen für die tomographische Wasserdampf-Rekonstruktion und 2) die Lösung des tomographischen Systems mittels Compressive Sensing. Zudem wird 3) die Qualität der CS-Rekonstruktion mit der Qualität üblicher LSQ-Schätzungen verglichen. Die tomographische Rekonstruktion der durch feuchte Refraktivitäten beschriebenen atmosphärischen Wasserdampf-Verteilung beruht auf der einen Seite auf realen feuchten Laufzeitverzögerungen aus GNSS und InSAR und auf der anderen Seite auf drei verschiedenen synthetischen Datensätzen feuchter Laufzeitverzögerungen, die aus Wasserdampf-Simulationen des Weather Research and Forecasting (WRF) Modells abgeleitet wurden. Die Validierung der geschätzten Wasserdampf-Verteilung stützt sich somit zum einen auf Radiosonden Profile und zum anderen auf einen Vergleich der geschätzten Refraktivitäten mit den WRF Refraktivitäten, die zugleich als Eingangsdaten zur Generierung der synthetischen Laufzeitverzögerungen genutzt werden. Der reale bzw. der erste synthetische Datensatz vergleicht die Rekonstruktionsqualität des entwickelten CS-Ansatzes mit üblichen Kleinste Quadrate Wasserdampf-Schätzungen und untersucht, inwieweit die Nutzung von InSAR Laufzeitverzögerungen bzw. von synthetischen InSAR Laufzeitverzögerungen die Genauigkeit und die Präzision der Wasserdampf-Rekonstruktion erhöht. Der zweite synthetische Datensatz wurde dafür ausgelegt, den allgemeinen Einfluss der Beobachtungsgeometrie auf die Refraktivitätsschätzungen zu analysieren. Der dritte synthetische Datensatz untersucht insbesondere die Empfindlichkeit der tomographischen Rekonstruktion gegenüber variierenden GNSS-Stationszahlen, unterschiedlichen Voxel-Diskretisierungen und verschiedenen Orbit-Konstellationen. Im realen Datensatz verhalten sich die Kleinste Quadrate Schätzung und der Compressive Sensing Ansatz sowohl für die reine GNSS-Lösung als auch für die kombinierte GNSS- und InSAR-Lösung konsistent. Die synthetischen Datensätze zeigen, dass Compressive Sensing in geeigneten Szenarien sehr genaue und präzise Ergebnisse liefern kann. Die Qualität der Wasserdampf-Schätzungen hängt in erster Linie ab i) von der Genauigkeit des funktionalen Modells, das die feuchten Laufzeitverzögerungen, die zu schätzenden Refraktivitäten und die von den Strahlen in den Voxeln zurückgelegten Distanzen in Beziehung zueinander setzt, ii) von der Anzahl verfügbarer GNSS Stationen, iii) von der Voxel-Diskretisierung, und iv) von der Vielseitigkeit der in das tomographische System eingebauten Strahlrichtungen. Die mittels des realen Datensatzes bzw. mittels der synthetischen Datensätze untersuchten Regionen sind etwa 120 × 120 km2 bzw. 100 × 100 km2 groß. Im realen Datensatz stehen acht GNSS-Stationen zur Verfügung und es werden feuchte Laufzeitverzögerungen von GPS InSAR genutzt. In den synthetischen Datensätzen werden unterschiedliche Stationsanzahlen definiert und vielseitige Strahlrichtungen getestet

Geodetic TomoSAR – Fusion of SAR Imaging Geodesy and TomoSAR for 3D absolute Scatterer Positioning

In this paper, we propose a framework referred to as “geodetic TomoSAR“ that fuses the SAR image geodesy and TomoSAR approaches to obtain absolute 3D positions of a large amount of natural scatterers. The methodology is applied on four Very High Resolution (VHR) TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since the TomoSAR estimates are referred to the identical reference point whose absolute 3D positions are retrieved by means of Stereo-SAR, the point clouds from ascending and descending orbits are automatically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3D TomoSAR point clouds are compared to a DSM obtained by airborne LiDAR