Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

Radar interferometry

Inclusion of data uncertainty in machine learning and its application in geodetic data science, with case studies for the prediction of Earth orientation parameters and GNSS station coordinate time series

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25 μm can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

MIMO-SAR Interferometric Measurements for Structural Monitoring: Accuracy and Limitations

Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking.

Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier

Full coverage and continuous deformation information retrieval are key aspects for dam health diagnosis Ground-based synthetic aperture radar (GB-SAR) interferometry is used for the remote monitoring of the Geheyan Dam, China Although the monitoring of a dam with ground-based interferometry is not an innovation, specific issues have been found out in the case study discussed due to the large dimension of the monitored structure More than 400 images were used for interferogram generation Radar signals reflected from the dam were carefully analyzed: a sort of tunneling effect caused by multireflection is observed, and deformations caused by water level and temperature variations were detected during a six-day monitoring campaign Radar monitoring results were compared to the data recorded by plummets installed in the dam The agreement between the displacements retrieved from interferometric data and the plummets demonstrates the capability of GB-SAR for deformation monitoring, with the advantage of large area coverage

Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China

Abstract The benefits of terrestrial radar interferometry (TRI) for deformation monitoring are restricted by the influence of changing meteorological conditions contaminating the potentially highly precise measurements with spurious deformations. This is especially the case when the measurement setup includes long distances between instrument and objects of interest and the topography affecting atmospheric refraction is complex. These situations are typically encountered with geo-monitoring in mountainous regions, e.g. with glaciers, landslides or volcanoes. We propose and explain an approach for the mitigation of atmospheric influences based on the theory of intrinsic random functions of order k (IRF-k) generalizing existing approaches based on ordinary least squares estimation of trend functions. This class of random functions retains convenient computational properties allowing for rigorous statistical inference while still permitting to model stochastic spatial phenomena which are non-stationary in mean and variance. We explore the correspondence between the properties of the IRF-k and the properties of the measurement process. In an exemplary case study, we find that our method reduces the time needed to obtain reliable estimates of glacial movements from 12 h down to 0.5 h compared to simple temporal averaging procedures.

Intrinsic random functions for mitigation of atmospheric effects in terrestrial radar interferometry

Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100 Hz to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175 m. Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1 m at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24 Hz. The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys.

/pdf/mimo-sar-interferometric-measurements-for-wind-turbine-tower-3rde6fs0.pdf

MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring

Self-calibration of terrestrial laser scanner using a M3C2-based planar patch algorithm

Polarimetric LiDAR combines polarimetry and non-coherent optical ranging techniques to complement the acquisition of geometrical information with material characteristics. In recent decades, polarimetric LiDAR has been widely explored in material probing, target detection, and object identification. These approaches have so far mainly relied on implementations using a single or very few wavelengths. In this work, we propose, develop and evaluate a polarimetric femtosecond-laser LiDAR that enables extracting multispectral polarization signatures on 7 spectral channels of 40 nm spectral bandwidth and 33 spectral channels of 10 nm spectral bandwidth in the visible and near-infrared range. Multispectral polarization signatures of five material specimens (cardboard, foam, plaster, plastic, and wood board) are obtained and used as input features on a linear support vector machine classification algorithm. The results show that extending polarimetric probing to multiple spectral channels improves the classification capabilities with respect to single-wavelength approaches. The combination of different spectral signature dimensions (polarization, reflectance, and distance) that can be derived from LiDAR measurements is also analyzed, with results indicating their capability to support challenging classification tasks.

Polarimetric femtosecond-laser LiDAR for multispectral material probing

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