14 research outputs found

    Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

    Get PDF
    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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

    Get PDF
    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

    Get PDF
    This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

    Maritime Transport ‘16

    Get PDF

    Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

    Get PDF
    The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

    Principal slope estimation at SAR building layovers

    Get PDF
    Spectral estimation is considered in the paper as an additional instrument towards a better understanding of the physical phenomena behind the layover scattering decomposition. A super-resolution technique is employed to derive the fringe frequencies characterizing the layover portion. Due to the limited estimation support, only the dominant frequency is found to be reliable information. The non-linear relationship with slopes is employed to derive a principal slope map. A bistatic interferometric scenario is tested. It is found that for the majority of the detections the facade contribution is the prevailing one due to the presence of targets with a high backscattered signal return at the vertical slope. The number of layover contributors is assessed prior to the spectral estimation. It has been estimated that the signal return is dominated by a single contribution for the majority of the layovers

    Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

    Get PDF
    Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps

    Tsunami Risk and Vulnerability

    Get PDF
    The research focuses on providing reliable spatial information in support of tsunami risk and vulnerability assessment within the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) project. It contributes to three major components of the project: (1) the provision of spatial information on surface roughness as an important parameter for tsunami inundation modeling and hazard assessment; (2) the modeling of population distribution, which is an essential factor in tsunami vulnerability assessment and local disaster management activities; and (3) the settlement detection and classification from remote sensing radar imagery to support the population distribution research. Regarding the surface roughness determination, research analyses on surface roughness classes and their coefficients have been conducted. This included the development of remote sensing classification techniques to derive surface roughness classes, and integration of the thus derived spatial information on surface roughness conditions to tsunami inundation modeling. This research determined 12 classes of surface roughness and their respective coefficients based on analyses of published values. The developed method for surface roughness classification of remote sensing data considered density and neighborhood conditions, and resulted in more than 90% accuracy. The classification method consists of two steps: main land use classification and density and neighborhood analysis. First, the main land uses were defined and a classification was performed applying decision tree modeling. Texture parameters played an important role in increasing the classification accuracy. The density and neighborhood analysis further substantiated the classification result towards identifying surface roughness classes. Different classes such as residential areas and trees were combined to new surface roughness classes, as “residential areas with trees”. The density and neighborhood analysis led to an appropriate representation of real surface roughness conditions. This was used as an important input for tsunami inundation modeling. By using Tohoku University’s Analysis Model for Investigation Near-field Tsunami Number 3 (TUNAMI N3), the spatially distributed surface roughness information was integrated in tsunami inundation modeling and compared to the modeling results applying a uniform surface roughness condition. An uncertainty analysis of tsunami inundation modeling based on the variation of surface roughness coefficients in the Cilacap study area was also undertaken. It was demonstrated that the inundation modeling results applying uniform and spatially distributed surface roughness resulted in high differences of inundation lengths, especially in areas far from the coastline. This result showed the important role of surface roughness conditions in resisting tsunami flow, which must be considered in tsunami inundation modeling. With respect to the second research focus, the population distribution, a concept of population distribution modeling was developed. Within the modeling process, weighting factor determination, multi-scale disaggregation and a comparative study to other methods were conducted. The basis of the developed method was a combination of census and land use data, which led to an improved spatial resolution and accuracy of the population distribution. Socio-economic data were used to derive weighting factors to distributing people to land use classes. Moreover, in case of missing input data, an approach was developed that allows for the determination of generalized weighting factors. The approach to use specific weightings, where possible and generalized ones, where necessary, led to a flexible methodology with respect to the achievable accuracy and availability of data. A comparative study was performed by comparing this new model with previously developed population distribution models. The newly developed model showed a higher accuracy. The detailed population distribution information was a valuable input for the vulnerability assessment being the main data source for human exposure assessment and an important contribution to evacuation time modeling. In support of the population distribution research, settlement classification using TerraSAR-X imagery was conducted. A current classification method of speckle divergence analysis on SAR imagery was further developed and improved by including the neighborhood concept. The settlement classification provided highly accurate results in dense urban areas, whereas the method needs to be further developed and improved for rural settlement areas. Finally, it has been shown how the results of this research can be applied. These applications cover the integration of surface roughness conditions into the tsunami inundation modeling and hazard mapping. The contributions to tsunami vulnerability assessment and evacuation planning were shown. Additionally, the results were integrated into the decision support system of the Tsunami Early Warning Center in Jakarta
    corecore