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## Publications of year 2020

### Thesis

1. Albert Monteith. Temporal Characteristics of Boreal Forest Radar Measurements. PhD thesis, Chalmers University of Technology, 2020. Keyword(s): SAR Tomography, BorealScat.

@PhdThesis{phDThesisAlbertMonteithChalmers2020TemporalCharacteristicsOfForestRadarMonitoring,
author = {Albert Monteith},
school = {Chalmers University of Technology},
title = {Temporal Characteristics of Boreal Forest Radar Measurements},
year = {2020},
keywords = {SAR Tomography, BorealScat},
owner = {ofrey},
url = {https://research.chalmers.se/publication/518599/file/518599_Fulltext.pdf},

}


### Articles in journal or book chapters

1. Simone Baffelli, Othmar Frey, and Irena Hajnsek. Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier. IEEE Transactions on Geoscience and Remote Sensing, 58(11):7533-7556, November 2020. Keyword(s): Gamma Portable Radar Interferometer, GPRI, Pol-GPRI, Atmospheric modeling, Spaceborne radar, Atmospheric measurements, Radar interferometry, Delays, Phase measurement, Atmospheric modeling, atmospheric phase screen (APS), differential radar interferometry, terrestrial radar interferometry, TRI.
 Abstract: 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.

@Article{baffelliFreyHajnsekTGRS2020GeostatisticalAtmosphereAnalysisMitigationKuBandGPRIGlacier,
author = {Baffelli, Simone and Frey, Othmar and Hajnsek, Irena},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in {Ku}-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier},
year = {2020},
issn = {1558-0644},
month = nov,
number = {11},
pages = {7533-7556},
volume = {58},
abstract = {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.},
doi = {10.1109/TGRS.2020.2976656},
file = {:baffelliFreyHajnsekTGRS2020GeostatisticalAtmosphereAnalysisMitigationKuBandGPRIGlacier.pdf:PDF},
pdf = {http://www.ifu-sar.ethz.ch/otfrey/SARbibliography/myPapers/baffelliFreyHajnsekTGRS2020GeostatisticalAtmosphereAnalysisMitigationKuBandGPRIGlacier.pdf},

}


2. Angelique Benoit, Beatrice Pinel-Puyssegur, Romain Jolivet, and Cecile Lasserre. CorPhU: an algorithm based on phase closure for the correction of unwrapping errors in SAR interferometry. Geophysical Journal International, 221(3):1959-1970, 03 2020.
 Abstract: Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series.

@Article{benoitPinelPuyssegurJolivetLasserreGJI2020CorPhuAPhaseClosureAlgorithmForCorrectionOfInSARUnwrappingErrors,
author = {Benoit, Angelique and Pinel-Puyssegur, Beatrice and Jolivet, Romain and Lasserre, Cecile},
journal = {Geophysical Journal International},
title = {{CorPhU: an algorithm based on phase closure for the correction of unwrapping errors in {SAR} interferometry}},
year = {2020},
issn = {0956-540X},
month = {03},
number = {3},
pages = {1959-1970},
volume = {221},
abstract = {Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series.},
doi = {10.1093/gji/ggaa120},
file = {:benoitPinelPuyssegurJolivetLasserreGJI2020CorPhuAPhaseClosureAlgorithmForCorrectionOfInSARUnwrappingErrors.pdf:PDF},
owner = {ofrey},
url = {https://doi.org/10.1093/gji/ggaa120},

}


3. Victor Cazcarra-Bes, Matteo Pardini, Marivi Tello-Alonso, and K. P. Papathanassiou. Comparison of Tomographic SAR Reflectivity Reconstruction Algorithms for Forest Applications at L-band. IEEE Trans. Geosci. Remote Sens., 58(1):147-164, January 2020. Keyword(s): SAR Processing, SAR Tomography, Forestry, Synthetic aperture radar, Image reconstruction, Decorrelation, Estimation, Reconstruction algorithms, Capon beamforming (CB), compressive sensing (CS), forest applications, forest structure, Fourier beamforming (FB), L-band, synthetic aperture radar (SAR), tomography, airborne SAR.
 Abstract: SAR Processing, SAR Tomography, Forest structure is a key parameter for forest applications, but it is difficult to be estimated at the required spatial and temporal scales. In this context, synthetic aperture radar Tomography (TomoSAR) that allows, at lower frequencies, the 3-D imaging of natural volume scatterers with high spatial and temporal resolution may be a game changer. The aim of this article is to evaluate three TomoSAR algorithms, Fourier beamforming (FB), Capon beamforming (CB), and compressive sensing (CS) with respect to their performance in the reconstruction of the 3-D forest reflectivity. The implications of volumetric forest scattering, as well as the temporal decorrelation of scatterers, are analyzed. The algorithms are compared on a set of simulated scenarios and then evaluated on an experimental L-band data set composed by four acquisition dates, each one consisting of five tomographic tracks. The data were acquired in 2014, within a time span of two months, over the Traunstein forest (Germany) using the F-SAR system. Additionally, discrete airborne Lidar has been used for a qualitative evaluation. The results indicate that the CS reconstruction is, for many practical cases, superior when compared to FB or CB reconstructions as they achieve higher vertical resolution, especially in cases with a lower number of acquisitions and complex forest scenarios. By combining acquisitions performed at different days, the effect of temporal decorrelation on each algorithm for two different tomographic implementations (repeat-pass vs. single-pass) has been assessed. The results indicate that simultaneously acquired image pairs allow a better reconstruction of the 3-D forest reflectivity.

@Article{cazcarraBesPardiniTelloPapathanassiouTGRS2019ComparisonSARTOMOAlgorithmsForestLBand,
author = {Cazcarra-Bes, Victor and Pardini, Matteo and Tello-Alonso, Marivi and K. P. {Papathanassiou}},
journal = {IEEE Trans. Geosci. Remote Sens.},
title = {Comparison of Tomographic {SAR} Reflectivity Reconstruction Algorithms for Forest Applications at {L-}band},
year = {2020},
month = {jan},
number = {1},
pages = {147--164},
volume = {58},
abstract = {SAR Processing, SAR Tomography, Forest structure is a key parameter for forest applications, but it is difficult to be estimated at the required spatial and temporal scales. In this context, synthetic aperture radar Tomography (TomoSAR) that allows, at lower frequencies, the 3-D imaging of natural volume scatterers with high spatial and temporal resolution may be a game changer. The aim of this article is to evaluate three TomoSAR algorithms, Fourier beamforming (FB), Capon beamforming (CB), and compressive sensing (CS) with respect to their performance in the reconstruction of the 3-D forest reflectivity. The implications of volumetric forest scattering, as well as the temporal decorrelation of scatterers, are analyzed. The algorithms are compared on a set of simulated scenarios and then evaluated on an experimental L-band data set composed by four acquisition dates, each one consisting of five tomographic tracks. The data were acquired in 2014, within a time span of two months, over the Traunstein forest (Germany) using the F-SAR system. Additionally, discrete airborne Lidar has been used for a qualitative evaluation. The results indicate that the CS reconstruction is, for many practical cases, superior when compared to FB or CB reconstructions as they achieve higher vertical resolution, especially in cases with a lower number of acquisitions and complex forest scenarios. By combining acquisitions performed at different days, the effect of temporal decorrelation on each algorithm for two different tomographic implementations (repeat-pass vs. single-pass) has been assessed. The results indicate that simultaneously acquired image pairs allow a better reconstruction of the 3-D forest reflectivity.},
doi = {10.1109/TGRS.2019.2934347},
file = {:cazcarraBesPardiniTelloPapathanassiouTGRS2019ComparisonSARTOMOAlgorithmsForestLBand.pdf:PDF},
keywords = {SAR Processing, SAR Tomography, Forestry;Synthetic aperture radar;Image reconstruction;Decorrelation;Estimation;Reconstruction algorithms;Capon beamforming (CB);compressive sensing (CS);forest applications;forest structure;Fourier beamforming (FB);L-band;synthetic aperture radar (SAR);tomography, airborne SAR},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


4. M. Dalaison and R. Jolivet. A Kalman Filter Time Series Analysis Method for InSAR. Journal of Geophysical Research: Solid Earth, 125(7):e2019JB019150, 2020. Note: E2019JB019150 10.1029/2019JB019150.
 Abstract: Abstract Earth orbiting satellites, such as Sentinel 1A-B, build up an ever-growing set of synthetic aperture radar images of the ground. This conceptually allows for real-time monitoring of ground displacements using Interferometric Synthetic Aperture Radar (InSAR), notably in tectonically active regions such as fault zones or over volcanoes. We propose a Kalman filter for InSAR time series analysis (KFTS), an efficient method to rapidly update preexisting time series of displacement with data as they are made available, with limited computational cost. KFTS solves together for the evolution of phase change with time and for a parametrized model of ground deformation. Synthetic tests of the KFTS reveal exact agreement with the equivalent weighted least squares solution and a convergence of descriptive model parameter after the assimilation of about 1 year of data. We include the impact of sudden deformation events such as earthquakes or slow slip events on the time series of displacement. First tests of the KFTS on ENVISAT data over Mt. Etna (Sicily) and Sentinel 1 data around the Chaman fault (Afghanistan, Pakistan) show precise (±0.05 mm) retrieval of phase change when data are sufficient. Otherwise, the optimized parametrized model is used to forecast phase change. Good agreement is found with classic time series analysis solution and GPS-derived time series. Accurate estimates are conditioned to the proper parametrization of errors so that models and observations can be combined with their respective uncertainties. This new tool is freely available to process ongoing InSAR time series.

@Article{dalaisonJolivetJGR2020KalmanFilterTimeSeriesAnalysisMethodforInSAR,
author = {Dalaison, M. and Jolivet, R.},
journal = {Journal of Geophysical Research: Solid Earth},
title = {A Kalman Filter Time Series Analysis Method for InSAR},
year = {2020},
note = {e2019JB019150 10.1029/2019JB019150},
number = {7},
pages = {e2019JB019150},
volume = {125},
abstract = {Abstract Earth orbiting satellites, such as Sentinel 1A-B, build up an ever-growing set of synthetic aperture radar images of the ground. This conceptually allows for real-time monitoring of ground displacements using Interferometric Synthetic Aperture Radar (InSAR), notably in tectonically active regions such as fault zones or over volcanoes. We propose a Kalman filter for InSAR time series analysis (KFTS), an efficient method to rapidly update preexisting time series of displacement with data as they are made available, with limited computational cost. KFTS solves together for the evolution of phase change with time and for a parametrized model of ground deformation. Synthetic tests of the KFTS reveal exact agreement with the equivalent weighted least squares solution and a convergence of descriptive model parameter after the assimilation of about 1 year of data. We include the impact of sudden deformation events such as earthquakes or slow slip events on the time series of displacement. First tests of the KFTS on ENVISAT data over Mt. Etna (Sicily) and Sentinel 1 data around the Chaman fault (Afghanistan, Pakistan) show precise (±0.05 mm) retrieval of phase change when data are sufficient. Otherwise, the optimized parametrized model is used to forecast phase change. Good agreement is found with classic time series analysis solution and GPS-derived time series. Accurate estimates are conditioned to the proper parametrization of errors so that models and observations can be combined with their respective uncertainties. This new tool is freely available to process ongoing InSAR time series.},
doi = {https://doi.org/10.1029/2019JB019150},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB019150},
file = {:dalaisonJolivetJGR2020KalmanFilterTimeSeriesAnalysisMethodforInSAR.pdf:PDF},
owner = {ofrey},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JB019150},

}


5. Xie Hu, Roland Burgmann, Eric J. Fielding, and Hyongki Lee. Internal kinematics of the Slumgullion landslide (USA) from high-resolution UAVSAR InSAR data. Remote Sensing of Environment, 251:112057, 2020. Keyword(s): SAR Processing, Slumgullion landslide, Kinematic boundaries, Geomorphologic features, UAVSAR, InSAR, DInSAR, Airborne SAR, L-band, Surface Displacements, Deformation.
 Abstract: Landslides represent one of the most damaging natural hazards and often lead to unexpected casualties and property damage. They also continually modify our natural environment and landscapes. Knowledge of landslide systems is largely restricted by the stochastic nature, subjective interpretation and infrequent or spatially sparse surveying of landslides. Characterized by persistent daily movements of a couple of centimeters over multi-centennial timescales and a long narrow shape as long as ~4 km, the Slumgullion landslide in Colorado, USA represents an ideal natural laboratory to study slow-moving landslides. Here we demonstrate the capability of the highly accurate, spatially continuous airborne Synthetic Aperture Radar (SAR) system of the NASA Uninhabited Aerial Vehicle SAR (UAVSAR) to characterize the kinematic details of internal deformation of the Slumgullion landslide using SAR interferometry (InSAR). We develop a phase-based approach to automatically extract the boundaries of the mobile geological structures without unwrapping. Comparison with historic field observations from 1991 reveals the 40-m advance of the frontal toe and shift of an internal fault. The UAVSAR data also resolve an internal minislide (100 by 70 m), which moves more southerly than the main body at 5 mm/day in the lower part of the landslide. A Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) shows that the minislide is associated with the opening of a 30 by 10 m pull-apart basin and bounding strike-slip faults. These extensional structures, nearby incised streams, and steepened local slopes helped establish the kinematic environment for the formation of the secondary minislide. The disparity between the UAVSAR InSAR-derived horizontal moving directions and the LiDAR DEM-derived slope aspects suggest that while the surface topography governs the first-order orientation, the local kinematics is also subject to the variable nature of heterogeneous landslide materials and the irregular basal bedrock surface. The landslide velocity and precipitation show similar multi-annual variations. Our study demonstrates that the freely available, high-resolution UAVSAR data, have great potential for characterizing landslide kinematics and other small-scale geological and geomorphological processes.

@Article{huBurgmannFieldingLeeRSE2020UAVSARDisplacementSlumgullionLandslideDInSAR,
author = {Xie Hu and Roland Burgmann and Eric J. Fielding and Hyongki Lee},
journal = {Remote Sensing of Environment},
title = {Internal kinematics of the Slumgullion landslide (USA) from high-resolution UAVSAR InSAR data},
year = {2020},
issn = {0034-4257},
pages = {112057},
volume = {251},
abstract = {Landslides represent one of the most damaging natural hazards and often lead to unexpected casualties and property damage. They also continually modify our natural environment and landscapes. Knowledge of landslide systems is largely restricted by the stochastic nature, subjective interpretation and infrequent or spatially sparse surveying of landslides. Characterized by persistent daily movements of a couple of centimeters over multi-centennial timescales and a long narrow shape as long as ~4 km, the Slumgullion landslide in Colorado, USA represents an ideal natural laboratory to study slow-moving landslides. Here we demonstrate the capability of the highly accurate, spatially continuous airborne Synthetic Aperture Radar (SAR) system of the NASA Uninhabited Aerial Vehicle SAR (UAVSAR) to characterize the kinematic details of internal deformation of the Slumgullion landslide using SAR interferometry (InSAR). We develop a phase-based approach to automatically extract the boundaries of the mobile geological structures without unwrapping. Comparison with historic field observations from 1991 reveals the 40-m advance of the frontal toe and shift of an internal fault. The UAVSAR data also resolve an internal minislide (100 by 70 m), which moves more southerly than the main body at 5 mm/day in the lower part of the landslide. A Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) shows that the minislide is associated with the opening of a 30 by 10 m pull-apart basin and bounding strike-slip faults. These extensional structures, nearby incised streams, and steepened local slopes helped establish the kinematic environment for the formation of the secondary minislide. The disparity between the UAVSAR InSAR-derived horizontal moving directions and the LiDAR DEM-derived slope aspects suggest that while the surface topography governs the first-order orientation, the local kinematics is also subject to the variable nature of heterogeneous landslide materials and the irregular basal bedrock surface. The landslide velocity and precipitation show similar multi-annual variations. Our study demonstrates that the freely available, high-resolution UAVSAR data, have great potential for characterizing landslide kinematics and other small-scale geological and geomorphological processes.},
doi = {https://doi.org/10.1016/j.rse.2020.112057},
file = {:huBurgmannFieldingLeeRSE2020UAVSARDisplacementSlumgullionLandslideDInSAR.pdf:PDF},
keywords = {SAR Processing, Slumgullion landslide, Kinematic boundaries, Geomorphologic features, UAVSAR, InSAR, DInSAR, Airborne SAR, L-band, Surface Displacements, Deformation},
owner = {ofrey},
url = {http://www.sciencedirect.com/science/article/pii/S0034425720304272},

}


6. Y. Huang, F. Liu, Z. Chen, J. Li, and W. Hong. An Improved Map-Drift Algorithm for Unmanned Aerial Vehicle SAR Imaging. IEEE Geoscience and Remote Sensing Letters, pp 1-5, 2020. Keyword(s): Synthetic aperture radar, Unmanned aerial vehicles, Apertures, Azimuth, Trajectory, Electronics packaging, Doppler effect, Map-drift algorithm (MDA), motion compensation (MOCO), random sample consensus (RANSAC), unmanned aerial vehicle synthetic aperture radar (UAV SAR) imaging..
 Abstract: Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.

@Article{huangLiuChenLiHongGRSL2020MapDriftAutofocusForUAVborneSARImaging,
author = {Y. {Huang} and F. {Liu} and Z. {Chen} and J. {Li} and W. {Hong}},
journal = {IEEE Geoscience and Remote Sensing Letters},
title = {An Improved Map-Drift Algorithm for Unmanned Aerial Vehicle {SAR} Imaging},
year = {2020},
issn = {1558-0571},
pages = {1--5},
abstract = {Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.},
doi = {10.1109/LGRS.2020.3011973},
file = {:huangLiuChenLiHongGRSL2020MapDriftAutofocusForUAVborneSARImaging.pdf:PDF},
keywords = {Synthetic aperture radar;Unmanned aerial vehicles;Apertures;Azimuth;Trajectory;Electronics packaging;Doppler effect;Map-drift algorithm (MDA);motion compensation (MOCO);random sample consensus (RANSAC);unmanned aerial vehicle synthetic aperture radar (UAV SAR) imaging.},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


7. P. Hügler, T. Grebner, C. Knill, and C. Waldschmidt. UAV-Borne 2-D and 3-D Radar-Based Grid Mapping. IEEE Geosci. Remote Sens. Lett., pp 1-5, 2020. Keyword(s): Frequency-modulated continuous-wave radar, multiple-input multiple-output (MIMO) radar, occupancy grid map (OGM), unmanned aerial vehicle (UAV)..

@Article{huglerGrebnerKnillWaldschmidtGRSL2020UAVborne2Dand3DGridmapping,
author = {P. {H\"ugler} and T. {Grebner} and C. {Knill} and C. {Waldschmidt}},
journal = {IEEE Geosci. Remote Sens. Lett.},
title = {{UAV}-Borne {2-D} and {3-D} Radar-Based Grid Mapping},
year = {2020},
issn = {1558-0571},
pages = {1-5},
doi = {10.1109/LGRS.2020.3025109},
file = {:huglerGrebnerKnillWaldschmidtGRSL2020UAVborne2Dand3DGridmapping.pdf:PDF},
keywords = {Frequency-modulated continuous-wave radar;multiple-input multiple-output (MIMO) radar;occupancy grid map (OGM);unmanned aerial vehicle (UAV).},
owner = {ofrey},

}


8. Jorge Jorge Ruiz, Risto Vehmas, Juha Lemmetyinen, Josu Uusitalo, Janne Lahtinen, Kari Lehtinen, Anna Kontu, Kimmo Rautiainen, Riku Tarvainen, Jouni Pulliainen, and Jaan Praks. SodSAR: A Tower-Based 1-10 GHz SAR System for Snow, Soil and Vegetation Studies. Sensors, 20(22), 2020.
 Abstract: We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankyl\"a, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed.

@Article{ruizVehmasLemmetyinenUusitaloLahtinenLehtinenKontuRautiainenTarvainenPulliainenPraksSENSORS2020SodSARTowerBasedSARforSnowSoilVegetation,
author = {Jorge Ruiz, Jorge and Vehmas, Risto and Lemmetyinen, Juha and Uusitalo, Josu and Lahtinen, Janne and Lehtinen, Kari and Kontu, Anna and Rautiainen, Kimmo and Tarvainen, Riku and Pulliainen, Jouni and Praks, Jaan},
journal = {Sensors},
title = {{SodSAR}: A Tower-Based 1-10 {GHz} {SAR} System for Snow, Soil and Vegetation Studies},
year = {2020},
issn = {1424-8220},
number = {22},
volume = {20},
abstract = {We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankyl\"a, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed.},
article-number = {6702},
doi = {10.3390/s20226702},
file = {:ruizVehmasLemmetyinenUusitaloLahtinenLehtinenKontuRautiainenTarvainenPulliainenPraksSENSORS2020SodSARTowerBasedSARforSnowSoilVegetation.pdf:PDF},
owner = {ofrey},
url = {https://www.mdpi.com/1424-8220/20/22/6702},

}


9. S. Leinss, H. Löwe, M. Proksch, and A. Kontu. Modeling the evolution of the structural anisotropy of snow. The Cryosphere, 14(1):51-75, 2020.
 Abstract: The structural anisotropy of snow characterizes the spatially anisotropic distribution of the ice and air microstructure and is a key parameter for improving parameterizations of physical properties. To enable the use of the anisotropy in snowpack models as an internal variable, we propose a simple model based on a rate equation for the temporal evolution. The model is validated with a comprehensive set of anisotropy profiles and time series from X-ray microtomography (CT) and radar measurements. The model includes two effects, namely temperature gradient metamorphism and settling, and can be forced by any snowpack model that predicts temperature and density. First, we use CT time series from lab experiments to validate the proposed effect of temperature gradient metamorphism. Next, we use SNOWPACK simulations to calibrate the model with radar time series from the NoSREx campaigns in Sodankylï¿½, Finland. Finally we compare the simulated anisotropy profiles against field-measured full-depth CT profiles. Our results confirm that the creation of vertical structures is mainly controlled by the vertical water vapor flux through the snow volume. Our results further indicate a yet undocumented effect of snow settling on the creation of horizontal structures. Overall the model is able to reproduce the characteristic anisotropy variations in radar time series of four different winter seasons with a very limited set of calibration parameters.

@Article{leinssLoewePorkschKontuCryosphere2020ModelingStructuralAnisotropyOfSnow,
author = {Leinss, S. and L\"owe, H. and Proksch, M. and Kontu, A.},
journal = {The Cryosphere},
title = {Modeling the evolution of the structural anisotropy of snow},
year = {2020},
number = {1},
pages = {51--75},
volume = {14},
abstract = {The structural anisotropy of snow characterizes the spatially anisotropic distribution of the ice and air microstructure and is a key parameter for improving parameterizations of physical properties. To enable the use of the anisotropy in snowpack models as an internal variable, we propose a simple model based on a rate equation for the temporal evolution. The model is validated with a comprehensive set of anisotropy profiles and time series from X-ray microtomography (CT) and radar measurements. The model includes two effects, namely temperature gradient metamorphism and settling, and can be forced by any snowpack model that predicts temperature and density. First, we use CT time series from lab experiments to validate the proposed effect of temperature gradient metamorphism. Next, we use SNOWPACK simulations to calibrate the model with radar time series from the NoSREx campaigns in Sodankylï¿½, Finland. Finally we compare the simulated anisotropy profiles against field-measured full-depth CT profiles. Our results confirm that the creation of vertical structures is mainly controlled by the vertical water vapor flux through the snow volume. Our results further indicate a yet undocumented effect of snow settling on the creation of horizontal structures. Overall the model is able to reproduce the characteristic anisotropy variations in radar time series of four different winter seasons with a very limited set of calibration parameters.},
doi = {10.5194/tc-14-51-2020},
file = {:leinssLoewePorkschKontuCryosphere2020ModelingStructuralAnisotropyOfSnow.pdf:PDF},
owner = {ofrey},
url = {https://tc.copernicus.org/articles/14/51/2020/},

}


10. Dieter Luebeck, Christian Wimmer, Laila F. Moreira, Marlon Alcantara, Oré Gian, Juliana A. Gós, Luciano P. Oliveira, Bárbara Teruel, Leonardo S. Bins, Lucas H. Gabrielli, and Hugo E. Hernandez-Figueroa. Drone-borne Differential SAR Interferometry. Remote Sensing, 12(5), 2020.
 Abstract: Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.

@Article{luebeckEtAlMDPIRemoteSensLett2020UAVDroneDInSAR,
author = {Luebeck, Dieter and Wimmer, Christian and F. Moreira, Laila and Alcantara, Marlon and Or\'e Gian and A. G\'os, Juliana and P. Oliveira, Luciano and Teruel, B\'arbara and S. Bins, Leonardo and H. Gabrielli, Lucas and Hernandez-Figueroa, Hugo E.},
journal = {Remote Sensing},
title = {Drone-borne Differential {SAR} Interferometry},
year = {2020},
issn = {2072-4292},
number = {5},
volume = {12},
abstract = {Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.},
article-number = {778},
doi = {10.3390/rs12050778},
file = {:luebeckEtAlMDPIRemoteSensLett2020UAVDroneDInSAR.pdf:PDF},
owner = {ofrey},
url = {https://www.mdpi.com/2072-4292/12/5/778},

}


11. J. Matar, M. Rodriguez-Cassola, G. Krieger, P. Lï¿½pez-Dekker, and A. Moreira. MEO SAR: System Concepts and Analysis. IEEE Transactions on Geoscience and Remote Sensing, 58(2):1313-1324, February 2020. Keyword(s): geophysical equipment, radar imaging, remote sensing by radar, spaceborne radar, synthetic aperture radar, MEO-SAR systems, Earth observation, microwave remote sensing instruments, moderate resolution images, low Earth orbits, MEO satellites, medium-Earth-orbit SAR systems, meter-scale resolutions, kilometer-scale resolutions, typical imaging capabilities, Orbits, Sensitivity, Synthetic aperture radar, Antennas, Spatial resolution, Low earth orbit satellites, Coverage, medium-Earth-orbit (MEO) synthetic aperture radar (SAR), orbits, SAR, space radiation, system performance.
 Abstract: Existing microwave remote sensing instruments used for Earth observation face a clear tradeoff between spatial resolution and revisit times at global scales. The typical imaging capabilities of current systems range from daily observations at kilometer-scale resolutions provided by scatterometers to meter-scale resolutions at lower temporal rates (more than ten days) typical of synthetic aperture radars (SARs). A natural way to fill the gap between these two extremes is to use medium-Earth-orbit SAR (MEO-SAR) systems. MEO satellites are deployed at altitudes above the region of low Earth orbits (LEOs), ending at around 2000 km and below the geosynchronous orbits (GEOs) near 35 786 km. MEO SAR shows a clear potential to provide advantages in terms of spatial coverage, downlink visibility, and global temporal revisit times, e.g., providing moderate resolution images (some tens of meters) at daily rates. This article discusses the design tradeoffs of MEO SAR, including sensitivity and orbit selection. The use of these higher orbits opens the door to global coverage in one- to two-day revisit or continental/oceanic coverage with multidaily observations, making MEO SAR very attractive for future scientific missions with specific interferometric and polarimetric capabilities.

@Article{matarRodriguezCassolaKriegerLopezDekkerMoreiraTGRS2020MEOSARConcepts,
author = {J. {Matar} and M. {Rodriguez-Cassola} and G. {Krieger} and P. {Lï¿½pez-Dekker} and A. {Moreira}},
title = {MEO SAR: System Concepts and Analysis},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {2020},
volume = {58},
number = {2},
pages = {1313-1324},
month = {Feb},
issn = {1558-0644},
abstract = {Existing microwave remote sensing instruments used for Earth observation face a clear tradeoff between spatial resolution and revisit times at global scales. The typical imaging capabilities of current systems range from daily observations at kilometer-scale resolutions provided by scatterometers to meter-scale resolutions at lower temporal rates (more than ten days) typical of synthetic aperture radars (SARs). A natural way to fill the gap between these two extremes is to use medium-Earth-orbit SAR (MEO-SAR) systems. MEO satellites are deployed at altitudes above the region of low Earth orbits (LEOs), ending at around 2000 km and below the geosynchronous orbits (GEOs) near 35 786 km. MEO SAR shows a clear potential to provide advantages in terms of spatial coverage, downlink visibility, and global temporal revisit times, e.g., providing moderate resolution images (some tens of meters) at daily rates. This article discusses the design tradeoffs of MEO SAR, including sensitivity and orbit selection. The use of these higher orbits opens the door to global coverage in one- to two-day revisit or continental/oceanic coverage with multidaily observations, making MEO SAR very attractive for future scientific missions with specific interferometric and polarimetric capabilities.},
doi = {10.1109/TGRS.2019.2945875},
keywords = {geophysical equipment;radar imaging;remote sensing by radar;spaceborne radar;synthetic aperture radar;MEO-SAR systems;Earth observation;microwave remote sensing instruments;moderate resolution images;low Earth orbits;MEO satellites;medium-Earth-orbit SAR systems;meter-scale resolutions;kilometer-scale resolutions;typical imaging capabilities;Orbits;Sensitivity;Synthetic aperture radar;Antennas;Spatial resolution;Low earth orbit satellites;Coverage;medium-Earth-orbit (MEO) synthetic aperture radar (SAR);orbits;SAR;space radiation;system performance},
owner = {ofrey},

}


12. Gian Ore, Marlon S. Alcantara, Juliana A. Goes, Luciano P. Oliveira, Jhonnatan Yepes, Barbara Teruel, Valquiria Castro, Leonardo S. Bins, Felicio Castro, Dieter Luebeck, Laila F. Moreira, Lucas H. Gabrielli, and Hugo E. Hernandez-Figueroa. Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sensing, 12(4), 2020. Keyword(s): SAR Processing, Interferometry, SAR Interferometry, UAV, Drone, Hexacopter, DJI, DJI-S900, Synthetic aperture radar (SAR), SAR interferometry, mobile mapping, UAV, airborne SAR, repeat-pass interferometry, differential interferometry, DInSAR, SAR imaging, focusing, back-projection, Time-Domain Back-Projection, TDBP,.
 Abstract: Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

@Article{oreEtAlREMOTESENSING2020CropGrowthWithDroneBorneDInSAR,
author = {Ore, Gian and Alcantara, Marlon S. and Goes, Juliana A. and Oliveira, Luciano P. and Yepes, Jhonnatan and Teruel, Barbara and Castro, Valquiria and Bins, Leonardo S. and Castro, Felicio and Luebeck, Dieter and Moreira, Laila F. and Gabrielli, Lucas H. and Hernandez-Figueroa, Hugo E.},
journal = {Remote Sensing},
title = {Crop Growth Monitoring with Drone-Borne {DInSAR}},
year = {2020},
issn = {2072-4292},
number = {4},
volume = {12},
abstract = {Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.},
article-number = {615},
doi = {10.3390/rs12040615},
file = {:oreEtAlREMOTESENSING2020CropGrowthWithDroneBorneDInSAR.pdf:PDF},
keywords = {SAR Processing, Interferometry, SAR Interferometry, UAV, Drone, Hexacopter, DJI, DJI-S900, Synthetic aperture radar (SAR), SAR interferometry, mobile mapping, UAV,airborne SAR, repeat-pass interferometry, differential interferometry, DInSAR, SAR imaging, focusing, back-projection,Time-Domain Back-Projection, TDBP,},
owner = {ofrey},
url = {https://www.mdpi.com/2072-4292/12/4/615},

}


 Abstract: Cross-track synthetic aperture radar (SAR) interferometry is a powerful technique that analyzes the phase shift each pixel undergoes between acquisitions of the same scene with just a slight change of viewpoint. These phase shifts provide information about the topography and, when more than two acquisitions are available at different dates, about possible slow motions along the line of sight, related to subsidence and/or thermal dilation, of the dominant scatterer in the resolution cell. However, interferometric processing exploits phase-only data, does not provide scatterer distribution in the vertical direction, and is not able to separate multiple scatterers lying in the same range/azimuth resolution cell.

@Article{rambourBudillonJohnsyDenisTupinSchirinziIEEEGRSMag2020FromInterferometricToTomographicSARaReview,
author = {C. {Rambour} and A. {Budillon} and A. C. {Johnsy} and L. {Denis} and F. {Tupin} and G. {Schirinzi}},
journal = {IEEE Geoscience and Remote Sensing Magazine},
title = {From Interferometric to Tomographic {SAR}: A Review of Synthetic Aperture Radar Tomography-Processing Techniques for Scatterer Unmixing in Urban Areas},
year = {2020},
issn = {2168-6831},
month = {June},
number = {2},
pages = {6-29},
volume = {8},
abstract = {Cross-track synthetic aperture radar (SAR) interferometry is a powerful technique that analyzes the phase shift each pixel undergoes between acquisitions of the same scene with just a slight change of viewpoint. These phase shifts provide information about the topography and, when more than two acquisitions are available at different dates, about possible slow motions along the line of sight, related to subsidence and/or thermal dilation, of the dominant scatterer in the resolution cell. However, interferometric processing exploits phase-only data, does not provide scatterer distribution in the vertical direction, and is not able to separate multiple scatterers lying in the same range/azimuth resolution cell.},
doi = {10.1109/MGRS.2019.2957215},
file = {:rambourBudillonJohnsyDenisTupinSchirinziIEEEGRSMag2020FromInterferometricToTomographicSARaReview.pdf:PDF},
owner = {ofrey},

}


14. M. Schartel, R. Burr, W. Mayer, and C. Waldschmidt. Airborne Tripwire Detection Using a Synthetic Aperture Radar. IEEE Geoscience and Remote Sensing Letters, 17(2):262-266, February 2020. Keyword(s): SAR Processing, UAV, FMCW, Chirp, Synthetic aperture radar, Radar imaging, Wires, Antennas, Radar cross-sections, Antipersonnel (AP) mine, frequency-modulated continuous-wave (FMCW) radar, multicopter, synthetic aperture radar (SAR), tripwire, unmanned aerial system (UAS), Time-Domain Back-Projection, TDBP.
 Abstract: Antipersonnel fragmentation mines are relatively large metallic mines, which are only partially buried and often triggered by a metallic tripwire. In humanitarian mine clearance, the search for the wires is usually carried out manually. As a new approach, an airborne system for the detection of tripwires using a synthetic aperture radar is presented. The system consists of an industrial multicopter, a frequency-modulated continuous-wave radar, and a real time kinematic global navigation satellite system. For image formation, a backprojection algorithm is used. Measurements with tripwires attached to a dummy mine successfully demonstrate the functionality of this system approach. In addition, the influence of wire length, vegetation, and incidence angle are investigated. It is shown that several overflights with different directions of flight are required to detect randomly oriented tripwires.

@Article{schartelBurrMayerWaldschmidtGRSL2020UAVSARMineDetection,
author = {M. {Schartel} and R. {Burr} and W. {Mayer} and C. {Waldschmidt}},
title = {Airborne Tripwire Detection Using a Synthetic Aperture Radar},
journal = {IEEE Geoscience and Remote Sensing Letters},
year = {2020},
volume = {17},
number = {2},
pages = {262-266},
month = {Feb},
issn = {1558-0571},
abstract = {Antipersonnel fragmentation mines are relatively large metallic mines, which are only partially buried and often triggered by a metallic tripwire. In humanitarian mine clearance, the search for the wires is usually carried out manually. As a new approach, an airborne system for the detection of tripwires using a synthetic aperture radar is presented. The system consists of an industrial multicopter, a frequency-modulated continuous-wave radar, and a real time kinematic global navigation satellite system. For image formation, a backprojection algorithm is used. Measurements with tripwires attached to a dummy mine successfully demonstrate the functionality of this system approach. In addition, the influence of wire length, vegetation, and incidence angle are investigated. It is shown that several overflights with different directions of flight are required to detect randomly oriented tripwires.},
doi = {10.1109/LGRS.2019.2917917},
file = {:schartelBurrMayerWaldschmidtGRSL2020UAVSARMineDetection.pdf:PDF},
owner = {ofrey},

}


15. Endrit Shehaj, Karina Wilgan, Othmar Frey, and Alain Geiger. A Collocation Framework to Retrieve Tropospheric Delays from a Combination of GNSS and InSAR. Navigation, 67(4):823-842, 2020. Keyword(s): SAR Processing, InSAR, SAR Interferometry, Persistent Scatterer Interferometry, PSI, GNSS, GPS Troposphere, Collocation, Retrieval of Tropospheric Delays, Combination of GNSS and InSAR.
 Abstract: High spatio-temporal variability of atmospheric water vapor affects microwave signals of Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). A better knowledge of the distribution of water vapor in the atmosphere improves both GNSS and InSAR derived data products. Moreover, this information can potentially enhance meteorological and climatological applications. In this work, we present a collocation framework to combine and retrieve zenith and (relative) slant tropospheric delays. GNSS and InSAR meteorological products, which are complementary in terms of spatio-temporal resolution, are combined aiming at a better retrieval of the atmospheric water vapor (and therefore of the respective delays). We investigate the combination approach with synthetic and real data acquired in the Alpine region of Switzerland. This research is a contribution to improve the spatio-temporal mapping of tropospheric delays by combining GNSS-derived and InSAR-derived delays.

@Article{shehajWilganFreyGeigerNavigation2020CollocationFrameworkForTropoRetrievalFromGNSSandInSAR,
author = {Shehaj, Endrit and Wilgan, Karina and Frey, Othmar and Geiger, Alain},
title = {A Collocation Framework to Retrieve Tropospheric Delays from a Combination of {GNSS} and {InSAR}},
year = {2020},
number = {4},
pages = {823-842},
volume = {67},
abstract = {High spatio-temporal variability of atmospheric water vapor affects microwave signals of Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). A better knowledge of the distribution of water vapor in the atmosphere improves both GNSS and InSAR derived data products. Moreover, this information can potentially enhance meteorological and climatological applications. In this work, we present a collocation framework to combine and retrieve zenith and (relative) slant tropospheric delays. GNSS and InSAR meteorological products, which are complementary in terms of spatio-temporal resolution, are combined aiming at a better retrieval of the atmospheric water vapor (and therefore of the respective delays). We investigate the combination approach with synthetic and real data acquired in the Alpine region of Switzerland. This research is a contribution to improve the spatio-temporal mapping of tropospheric delays by combining GNSS-derived and InSAR-derived delays.},
doi = {10.1002/navi.398},
keywords = {SAR Processing, InSAR, SAR Interferometry, Persistent Scatterer Interferometry, PSI, GNSS, GPS Troposphere, Collocation, Retrieval of Tropospheric Delays, Combination of GNSS and InSAR},
owner = {ofrey},

}


 Abstract: This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopï¿½ National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH-VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements.

@Article{shiromaLavalleTGRS2020DigitalTerrainSurfaceAndCanopyHeightFromInSARBackscatterHeightHistograms,
author = {G. H. X. {Shiroma} and M. {Lavalle}},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Digital Terrain, Surface, and Canopy Height Models From InSAR Backscatter-Height Histograms},
year = {2020},
issn = {1558-0644},
month = {June},
number = {6},
pages = {3754-3777},
volume = {58},
abstract = {This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopï¿½ National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH-VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements.},
doi = {10.1109/TGRS.2019.2956989},
file = {:shiromaLavalleTGRS2020DigitalTerrainSurfaceAndCanopyHeightFromInSARBackscatterHeightHistograms.pdf:PDF},
owner = {ofrey},

}


17. F. Sica, G. Gobbi, Paola Rizzoli, and Lorrenzo Bruzzone. arphi-Net: Deep Residual Learning for InSAR Parameters Estimation. IEEE Transactions on Geoscience and Remote Sensing, pp 1-25, 2020. Keyword(s): Estimation, Synthetic aperture radar, Coherence, Noise reduction, Convolution, Measurement, Wavelet transforms, Coherence, convolutional neural network (CNN), deep learning (DL), denoising, interferometric phase, residual learning, synthetic aperture radar (SAR) interferometry..
 Abstract: Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named \varphi-Net, for the joint estimation of the interferometric phase and coherence. \varphi-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the \varphi-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that \varphi-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

@Article{sicaGobbiRizzoliBruzzoneTGRS2020DeepResidualLearningForInSARParameterEstimation,
author = {F. {Sica} and G. {Gobbi} and Paola {Rizzoli} and Lorrenzo {Bruzzone}},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {\varphi-Net: Deep Residual Learning for InSAR Parameters Estimation},
year = {2020},
issn = {1558-0644},
pages = {1-25},
abstract = {Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named \varphi-Net, for the joint estimation of the interferometric phase and coherence. \varphi-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the \varphi-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that \varphi-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.},
doi = {10.1109/TGRS.2020.3020427},
keywords = {Estimation;Synthetic aperture radar;Coherence;Noise reduction;Convolution;Measurement;Wavelet transforms;Coherence;convolutional neural network (CNN);deep learning (DL);denoising;interferometric phase;residual learning;synthetic aperture radar (SAR) interferometry.},

}


18. Ladina Steiner, Michael Meindl, Christoph Marty, and Alain Geiger. Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals. IEEE Transactions on Geoscience and Remote Sensing, 58(1):123-135, January 2020. Keyword(s): Global Positioning System, remote sensing, snow, Swiss Alps, GPS processing parameters, SWE estimation performance, elevation-dependent weighting scheme, elevation cutoff angles, sub-snow GPS, temporal reliability, systematic overview, seasons time period, GPS refractometry, sub-snow global positioning system antennas, snowpack modeling, remote sensing data, snow hydrological monitoring, weather conditions, continuous SWE quantification, automated SWE quantification, situ snow water equivalent estimation, global navigation satellite system antennas, refracted GPS signals, daily estimates, hourly SWE estimation, Global Positioning System, Snow, Estimation, Global navigation satellite system, Antennas, Satellites, Delays, Global navigation satellite system (GNSS), global positioning system (GPS), GPS refractometry, snow, snow water equivalent (SWE), sub-snow.
 Abstract: Global navigation satellite system (GNSS) antennas buried underneath a snowpack have a high potential for in situ snow water equivalent (SWE) estimation. Automated and continuous SWE quantification independent of weather conditions could enhance snow hydrological monitoring and modeling. Accurate and reliable in situ data are needed for the calibration and validation of remote sensing data and snowpack modeling. A relative bias of less than 5% is achieved using sub-snow global positioning system (GPS) antennas (GPS refractometry) during a three full seasons time period in the Swiss Alps. A systematic overview regarding the temporal reliability of the sub-snow GPS derived results is, however, missing for this emerging technique. Moreover, GPS processing impacts the results significantly. Different GPS processing parameters are therefore selected and their influence on the SWE estimation is investigated. The impact of elevation-dependent weighting, the elevation cutoff angles, and the time intervals for SWE estimation are systematically assessed. The best results are achieved using all observations with an elevation-dependent weighting scheme. Moreover, the SWE estimation performance is equally accurate for hourly SWE estimation as for lower temporal resolutions up to daily estimates. The impact of snow on the coordinate solution is furthermore evaluated. While the east and north components are not systematically influenced by the overlying snowpack, the vertical component exhibits a significant variation and strongly depends on the SWE. The biased vertical component therefore provides an additional possibility to estimate SWE.

@Article{steinerMeindlMartyGeigerTGRS2020GNSSprocessingOfSnowWaterEquivalentSWE,
author = {Ladina Steiner and Michael Meindl and Christoph Marty and Alain Geiger},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals},
year = {2020},
issn = {1558-0644},
month = {Jan},
number = {1},
pages = {123-135},
volume = {58},
abstract = {Global navigation satellite system (GNSS) antennas buried underneath a snowpack have a high potential for in situ snow water equivalent (SWE) estimation. Automated and continuous SWE quantification independent of weather conditions could enhance snow hydrological monitoring and modeling. Accurate and reliable in situ data are needed for the calibration and validation of remote sensing data and snowpack modeling. A relative bias of less than 5% is achieved using sub-snow global positioning system (GPS) antennas (GPS refractometry) during a three full seasons time period in the Swiss Alps. A systematic overview regarding the temporal reliability of the sub-snow GPS derived results is, however, missing for this emerging technique. Moreover, GPS processing impacts the results significantly. Different GPS processing parameters are therefore selected and their influence on the SWE estimation is investigated. The impact of elevation-dependent weighting, the elevation cutoff angles, and the time intervals for SWE estimation are systematically assessed. The best results are achieved using all observations with an elevation-dependent weighting scheme. Moreover, the SWE estimation performance is equally accurate for hourly SWE estimation as for lower temporal resolutions up to daily estimates. The impact of snow on the coordinate solution is furthermore evaluated. While the east and north components are not systematically influenced by the overlying snowpack, the vertical component exhibits a significant variation and strongly depends on the SWE. The biased vertical component therefore provides an additional possibility to estimate SWE.},
doi = {10.1109/TGRS.2019.2934016},
file = {:steinerMeindlMartyGeigerTGRS2020GNSSprocessingOfSnowWaterEquivalentSWE.pdf:PDF},
keywords = {Global Positioning System;remote sensing;snow;Swiss Alps;GPS processing parameters;SWE estimation performance;elevation-dependent weighting scheme;elevation cutoff angles;sub-snow GPS;temporal reliability;systematic overview;seasons time period;GPS refractometry;sub-snow global positioning system antennas;snowpack modeling;remote sensing data;snow hydrological monitoring;weather conditions;continuous SWE quantification;automated SWE quantification;situ snow water equivalent estimation;global navigation satellite system antennas;refracted GPS signals;daily estimates;hourly SWE estimation;Global Positioning System;Snow;Estimation;Global navigation satellite system;Antennas;Satellites;Delays;Global navigation satellite system (GNSS);global positioning system (GPS);GPS refractometry;snow;snow water equivalent (SWE);sub-snow},
owner = {ofrey},

}


19. Benjamin Thomas, Alan Hunter, and Samantha Dugelay. Phase Wrap Error Correction by Random Sample Consensus With Application to Synthetic Aperture Sonar Micronavigation. IEEE Journal of Oceanic Engineering, pp 1-15, 2020. Keyword(s): SAR Processing, Time-Domain Back-Projection, TDBP, Delay effects, Synthetic aperture sonar, SAS, Estimation, Bandwidth, Synthetic aperture radar, Robustness, Phase unwrapping, synthetic aperture radar (SAR), synthetic aperture sonar (SAS), time delay estimation.
 Abstract: Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270-330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29%-125% and 30%-150%, respectively, compared to 16%-134% for the branch-cuts method for this data set.

@Article{thomasHunterDugelayIEEEJOE2020PhaseWrapErrorCorrectionSyntheticApertureSonar,
author = {Thomas, Benjamin and Hunter, Alan and Dugelay, Samantha},
journal = {IEEE Journal of Oceanic Engineering},
title = {Phase Wrap Error Correction by Random Sample Consensus With Application to Synthetic Aperture Sonar Micronavigation},
year = {2020},
issn = {1558-1691},
pages = {1-15},
abstract = {Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270-330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29%-125% and 30%-150%, respectively, compared to 16%-134% for the branch-cuts method for this data set.},
doi = {10.1109/JOE.2019.2960582},
file = {:thomasHunterDugelayIEEEJOE2020PhaseWrapErrorCorrectionSyntheticApertureSonar.pdf:PDF},
keywords = {SAR Processing, Time-Domain Back-Projection , TDBP, Delay effects;Synthetic aperture sonar; SAS, Estimation;Bandwidth;Synthetic aperture radar;Robustness;Phase unwrapping;synthetic aperture radar (SAR);synthetic aperture sonar (SAS);time delay estimation},

}


20. Simona Verde, Antonio Pauciullo, Diego Reale, and Gianfranco Fornaro. Multiresolution Detection of Persistent Scatterers: A Performance Comparison Between Multilook GLRT and CAESAR. IEEE Transactions on Geoscience and Remote Sensing, pp 1-16, 2020. Keyword(s): Monitoring, Detectors, Spatial resolution, Signal resolution, Tomography, Covariance matrices, Strain, Detection, generalized likelihood ratio test (GLRT), persistent scatterers (PS), SAR tomography..
 Abstract: Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations.

@Article{verdePauciulloRealeFornaroTGRS2020PSIPerformanceComparisonMultilookGLRTandCAESAR,
author = {Simona {Verde} and Antonio {Pauciullo} and Diego {Reale} and Gianfranco {Fornaro}},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Multiresolution Detection of Persistent Scatterers: A Performance Comparison Between Multilook GLRT and CAESAR},
year = {2020},
issn = {1558-0644},
pages = {1-16},
abstract = {Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations.},
doi = {10.1109/TGRS.2020.3007927},
file = {:verdePauciulloRealeFornaroTGRS2020PSIPerformanceComparisonMultilookGLRTandCAESAR.pdf:PDF},
keywords = {Monitoring;Detectors;Spatial resolution;Signal resolution;Tomography;Covariance matrices;Strain;Detection;generalized likelihood ratio test (GLRT);persistent scatterers (PS);SAR tomography.},

}


21. Yanghai Yu, Mauro Mariotti d'Alessandro, Stefano Tebaldini, and Mingsheng Liao. Signal Processing Options for High Resolution SAR Tomography of Natural Scenarios. Remote Sensing, 12(10), 2020.
 Abstract: Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.

@Article{yuDAlessandroTebaldiniREMOTESENSING2020SignalProcessingOptionsHighResolutionSARTomographyNaturalMedia,
author = {Yu, Yanghai and d'Alessandro, Mauro Mariotti and Tebaldini, Stefano and Liao, Mingsheng},
journal = {Remote Sensing},
title = {Signal Processing Options for High Resolution {SAR} Tomography of Natural Scenarios},
year = {2020},
issn = {2072-4292},
number = {10},
volume = {12},
abstract = {Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.},
article-number = {1638},
doi = {10.3390/rs12101638},
file = {:yuDAlessandroTebaldiniREMOTESENSING2020SignalProcessingOptionsHighResolutionSARTomographyNaturalMedia.pdf:PDF},
owner = {ofrey},
url = {https://www.mdpi.com/2072-4292/12/10/1638},

}


22. Y. Zhang, D. Zhu, X. Mao, X. Yu, J. Zhang, and Y. Li. Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing. IEEE Aerospace and Electronic Systems Magazine, 35(12):32-43, December 2020. Keyword(s): SAR Processing, SAR Imaging, SAR Focusing, UAV, Video SAR, Video sequences, Unmanned aerial vehicles, Trajectory, Vehicle dynamics, Synthetic aperture radar, Field programmable gate arrays, Video signal processing.
 Abstract: 360-degree video synthetic aperture radar (VideoSAR) presents a powerful capability for the information perception of dynamic scenes. In comparison to time-consuming airborne SAR, miniature SAR (MiniSAR) mounted on an unmanned aerial vehicle platform is a cost-effective configuration that provides a higher operation flexibility for video acquisition. Nevertheless, MiniSAR system development and signal processing limited by multiple factors are still challenging issues, including restricted weight, volume, power consumption, complicated trajectory imaging, and high-efficient video processing. In this article, we develop a multirotors VideoSAR system at X-band in Nanjing University of Aeronautics and Astronautics, concerning hardware architecture, field experiment, and video signal processing. It has the capability of ultrahigh resolution imaging with three available channels up to 1.8-GHz transmission bandwidth. Field programmable gate array-based unified signal processing architecture can further accelerate the generation of massive VideoSAR sequences in terms of both circular spotlight and stripmap modes. Several experimental results have been presented to demonstrate the validity of our developed system.

@Article{zhangZhuMaoYuZhangLiIEEEAESMagMultirotorsUAVVideoSAR,
author = {Y. {Zhang} and D. {Zhu} and X. {Mao} and X. {Yu} and J. {Zhang} and Y. {Li}},
journal = {IEEE Aerospace and Electronic Systems Magazine},
title = {Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing},
year = {2020},
issn = {1557-959X},
month = {Dec},
number = {12},
pages = {32-43},
volume = {35},
abstract = {360-degree video synthetic aperture radar (VideoSAR) presents a powerful capability for the information perception of dynamic scenes. In comparison to time-consuming airborne SAR, miniature SAR (MiniSAR) mounted on an unmanned aerial vehicle platform is a cost-effective configuration that provides a higher operation flexibility for video acquisition. Nevertheless, MiniSAR system development and signal processing limited by multiple factors are still challenging issues, including restricted weight, volume, power consumption, complicated trajectory imaging, and high-efficient video processing. In this article, we develop a multirotors VideoSAR system at X-band in Nanjing University of Aeronautics and Astronautics, concerning hardware architecture, field experiment, and video signal processing. It has the capability of ultrahigh resolution imaging with three available channels up to 1.8-GHz transmission bandwidth. Field programmable gate array-based unified signal processing architecture can further accelerate the generation of massive VideoSAR sequences in terms of both circular spotlight and stripmap modes. Several experimental results have been presented to demonstrate the validity of our developed system.},
doi = {10.1109/MAES.2020.3000318},
file = {:zhangZhuMaoYuZhangLiIEEEAESMagMultirotorsUAVVideoSAR.pdf:PDF},
keywords = {SAR Processing, SAR Imaging, SAR Focusing, UAV, Video SAR, Video sequences;Unmanned aerial vehicles;Trajectory;Vehicle dynamics;Synthetic aperture radar;Field programmable gate arrays;Video signal processing},
owner = {ofrey},

}


### Conference articles

1. Othmar Frey, Charles Werner, Andrea Manconi, and Roberto Coscione. Measuring surface displacements using a novel UAV/car-borne radar interferometer: including a case study on a fast-moving landslide in Brinzauls. In Swiss Geoscience Meeting 2020: Symposium 20. Remote Sensing of the Spheres, Zurich, Switzerland, November 2020.
@InProceedings{freyWernerManconiCoscioneSGM2020UAVandCARSARmobilemappingDisplacements,
author = {Frey, Othmar and Werner, Charles and Manconi, Andrea and Coscione, Roberto},
booktitle = {Swiss Geoscience Meeting 2020: Symposium 20. Remote Sensing of the Spheres},
title = {Measuring surface displacements using a novel {UAV}/car-borne radar interferometer: including a case study on a fast-moving landslide in {Brinzauls}},
year = {2020},
month = nov,
owner = {ofrey},
url = {https://youtu.be/DxAvqFT_03I},

}


2. Othmar Frey, Charles Werner, Andrea Manconi, and Roberto Coscione. Mobile Mapping of Surface Displacements Using a Novel Compact UAV-Borne / Car-Borne InSAR System. In American Geophysical Union, Fall Meeting 2020, 2020.
 Abstract: Flexible mobile mapping of surface displacements with repeat-pass interferometry from moving platforms such as cars and UAVs has been a rather unexplored field. In this contribution we address this topic comprehensively: we demonstrate InSAR-based measurement of surface displacements with our novel car-borne and UAV-borne L-band SAR system setup at three different test sites in Switzerland. The reduced temporal decorrelation at L-band is an important advantage and a complementary property as compared to high-frequency (quasi-)stationary systems. While the sensitivity to line-of-sight displacements is lower, the longer wavelength permits to acquire longer interferometric time intervals also in natural terrain and in adverse conditions, in which the decorrelation time at X- or Ku-band (the frequencies of many stationary terrestrial radar interferometers) can be in the order of minutes or less. Terrestrial synthetic aperture radar acquisitions from a car driving on a road or acquisitions from a UAV allow to obtain synthetic aperture lengths of 100m and more which yields high-resolution SAR imagery also at lower frequency such as L-band. At the same time the view geometry can be chosen to offer line-of-sight views to landslides that complement the view geometries available from spaceborne SAR systems. Then, using a time-domain back-projection image focusing approach, it is ensured that even for curvilinear paths (e.g. a car driving along a curved road) high-quality SAR images and interferograms with good spatial resolution are obtained. Based on these properties we show that such a mobile InSAR system fills a current gap in terms of available InSAR systems for displacement monitoring. We show the potential and discuss the challenges and the limitations of this novel InSAR-based mobile mapping system. We do so with the help of three repeat-pass interferometry showcases (see also attached image): 1) car-borne mapping of surface displacements of fast-moving land slide and surrounding area, 2) car-borne mapping of surface displacements of a glacier, 3) UAV-borne mapping of surface displacements of a steep slope with various land covers. The three test cases show that UAV-borne and car-borne interferometric displacement measurements at L-band are feasible with high quality over various natural terrain.

@InProceedings{freyWernerManconiCoscioneAGU2020UAVandCARSARmobilemappingDisplacements,
author = {Frey, Othmar and Werner, Charles and Manconi, Andrea and Coscione, Roberto},
booktitle = {American Geophysical Union, Fall Meeting 2020},
title = {Mobile Mapping of Surface Displacements Using a Novel Compact {UAV}-Borne / Car-Borne {InSAR} System},
year = {2020},
abstract = {Flexible mobile mapping of surface displacements with repeat-pass interferometry from moving platforms such as cars and UAVs has been a rather unexplored field. In this contribution we address this topic comprehensively: we demonstrate InSAR-based measurement of surface displacements with our novel car-borne and UAV-borne L-band SAR system setup at three different test sites in Switzerland. The reduced temporal decorrelation at L-band is an important advantage and a complementary property as compared to high-frequency (quasi-)stationary systems. While the sensitivity to line-of-sight displacements is lower, the longer wavelength permits to acquire longer interferometric time intervals also in natural terrain and in adverse conditions, in which the decorrelation time at X- or Ku-band (the frequencies of many stationary terrestrial radar interferometers) can be in the order of minutes or less. Terrestrial synthetic aperture radar acquisitions from a car driving on a road or acquisitions from a UAV allow to obtain synthetic aperture lengths of 100m and more which yields high-resolution SAR imagery also at lower frequency such as L-band. At the same time the view geometry can be chosen to offer line-of-sight views to landslides that complement the view geometries available from spaceborne SAR systems. Then, using a time-domain back-projection image focusing approach, it is ensured that even for curvilinear paths (e.g. a car driving along a curved road) high-quality SAR images and interferograms with good spatial resolution are obtained. Based on these properties we show that such a mobile InSAR system fills a current gap in terms of available InSAR systems for displacement monitoring. We show the potential and discuss the challenges and the limitations of this novel InSAR-based mobile mapping system. We do so with the help of three repeat-pass interferometry showcases (see also attached image): 1) car-borne mapping of surface displacements of fast-moving land slide and surrounding area, 2) car-borne mapping of surface displacements of a glacier, 3) UAV-borne mapping of surface displacements of a steep slope with various land covers. The three test cases show that UAV-borne and car-borne interferometric displacement measurements at L-band are feasible with high quality over various natural terrain.},
owner = {ofrey},
url = {https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/726201},

}


3. Silvan Leinss, Shiyi Li, Philipp Bernhard, and Othmar Frey. Temporal Multi-Looking of SAR Image Series for Glacier Velocity Determination and Speckle Reduction. In EGU General Assembly 2020, volume EGU2020-3643, May 2020.
 Abstract: The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images. SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible. In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend. As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle. As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.

@InProceedings{leinssEtAlEGU2020Stacking,
author = {Silvan Leinss and Shiyi Li and Philipp Bernhard and Othmar Frey},
booktitle = {EGU General Assembly 2020},
title = {Temporal Multi-Looking of {SAR} Image Series for Glacier Velocity Determination and Speckle Reduction},
year = {2020},
month = may,
volume = {EGU2020-3643},
abstract = {The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images. SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible. In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend. As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle. As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.},
doi = {10.5194/egusphere-egu2020-3643},
owner = {ofrey},
url = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-3643.html},

}


4. A. Manconi, R. Caduff, T. Strozzi, O. Frey, Werner. C., and U. Wegmuller. Monitoring displacements of complex landslide with broadband multiplatform radar techniques. In Swiss Geoscience Meeting 2020: Symposium 20. Remote Sensing of the Spheres, Zurich, Switzerland, 2020.
@Conference{Manconi2020,
author = {Manconi, A. and Caduff, R. and Strozzi, T. and Frey, O. and Werner. C. and Wegmuller, U.},
booktitle = {Swiss Geoscience Meeting 2020: Symposium 20. Remote Sensing of the Spheres, Zurich, Switzerland},
title = {Monitoring displacements of complex landslide with broadband multiplatform radar techniques},
year = {2020},
issn = {https://youtu.be/EEyA5MI-JpM},
owner = {ofrey},

}


 Abstract: The paper describes the basic components of MirrorSAR and explains how bi- and multistatic SAR acquisitions are achieved by shifting only minimal functionality to the receive satellites. It shows that synchronization is not required or reduced in complexity by a MirrorLink. The MirrorLink forwards the ground reflected radar signal from the receiving satellites in a space transponder manner to the transmit satellite. Several options for the MirrorLink are discussed. In MirrorSAR, a number of satellites enable the acquisition of dual- or multi-baselines in a single-pass. The paper discusses several application examples where MirrorSAR eases the overall system complexity, the data acquisition and improves the SAR product quality. An interferometric SAR application example is discussed in more detail.

@InProceedings{mittermayerKriegerMoreiraIEEERadarCon2020MirrorSAR,
author = {J. {Mittermayer} and G. {Krieger} and A. {Moreira}},
title = {Concepts and Applications of Multi-static MirrorSAR Systems},
year = {2020},
month = {Sep.},
pages = {1-6},
abstract = {The paper describes the basic components of MirrorSAR and explains how bi- and multistatic SAR acquisitions are achieved by shifting only minimal functionality to the receive satellites. It shows that synchronization is not required or reduced in complexity by a MirrorLink. The MirrorLink forwards the ground reflected radar signal from the receiving satellites in a space transponder manner to the transmit satellite. Several options for the MirrorLink are discussed. In MirrorSAR, a number of satellites enable the acquisition of dual- or multi-baselines in a single-pass. The paper discusses several application examples where MirrorSAR eases the overall system complexity, the data acquisition and improves the SAR product quality. An interferometric SAR application example is discussed in more detail.},
issn = {2375-5318},
owner = {ofrey},

}


6. I. Walterscheid, P. Berens, M. Caris, S. Sieger, O. Saalmann, D. Janssen, G. El-Arnauti, A. Ribalta, D. Henke, and E. M. Dominguez. First results of a joint measurement campaign with PAMIR-Ka and MIRANDA-94. In 2020 IEEE Radar Conference (RadarConf20), pages 1-6, September 2020. Keyword(s): Radar, Radar imaging, Synthetic aperture radar, Radar polarimetry, Aircraft, Bandwidth, Radar antennas, Multi-dimensional radar imaging, Synthetic Aperture Radar, SAR, Multi-look SAR, Multi-aspect SAR, Polarimetric SAR, PAMIR-Ka, MIRANDA-94.
 Abstract: Fraunhofer FHR has participated in the international measurement campaign of the NATO research task group SET-250 with two airborne SAR systems in July 2019. The general objective of the trials was to investigate the use of multidimensional radar to increase the performance of radar imaging systems. The first system PAMIR-Ka is a multi-channel pulsed radar system operating at 34 GHz with a very high bandwidth of up to 8 GHz. The second system MIRANDA-94 is a multichannel frequency modulated continuous wave (FMCW) radar with up to 3 GHz at 94 GHz center frequency with a dual polarized antenna. The paper introduces the systems, explains the data collection, and presents first results with respect to multi-look, multi-frequency, multi-polarization, and multi-aspect radar imaging of a test site with military targets.

@InProceedings{walterscheidBerensCarisSiegerSaalmannJanssenElArnautiRibaltaHenkeDominguezIEEERadarCon2020PamirAndMIRANDA94,
author = {I. {Walterscheid} and P. {Berens} and M. {Caris} and S. {Sieger} and O. {Saalmann} and D. {Janssen} and G. {El-Arnauti} and A. {Ribalta} and D. {Henke} and E. M. {Dominguez}},
title = {First results of a joint measurement campaign with {PAMIR-Ka} and {MIRANDA-94}},
year = {2020},
month = sep,
pages = {1-6},
abstract = {Fraunhofer FHR has participated in the international measurement campaign of the NATO research task group SET-250 with two airborne SAR systems in July 2019. The general objective of the trials was to investigate the use of multidimensional radar to increase the performance of radar imaging systems. The first system PAMIR-Ka is a multi-channel pulsed radar system operating at 34 GHz with a very high bandwidth of up to 8 GHz. The second system MIRANDA-94 is a multichannel frequency modulated continuous wave (FMCW) radar with up to 3 GHz at 94 GHz center frequency with a dual polarized antenna. The paper introduces the systems, explains the data collection, and presents first results with respect to multi-look, multi-frequency, multi-polarization, and multi-aspect radar imaging of a test site with military targets.},
issn = {2375-5318},
owner = {ofrey},

}


7. Evan Zaugg, A. Margulis, M. Margulis, J. Bradley, A. Kozak, and J. Budge. Next-Generation Software Defined Radar: First Results. In 2020 IEEE International Radar Conference (RADAR), pages 749-754, April 2020. Keyword(s): aerospace testing, airborne radar, SlimSDR, flight testing, ARTEMIS SlimSAR, next-generation software defined radar, slim software defined radar.
 Abstract: ARTEMIS, Inc. has begun flight testing a new radar system called the SlimSDR (for Slim, Software Defined Radar). This successor to the ARTEMIS SlimSAR began test flights in September 2019. Like the SlimSAR, it is a compact radar system, but provides additional capabilities and flexibility. As a software defined radar, the SlimSDR is modular, multi-frequency, and applicable to multiple applications. This paper details the design and development process of the SlimSDR and shows initial results from the first flight tests of the system.

@InProceedings{zauggEtAl2020SoftwareDefinedRadioRadar,
author = {Evan Zaugg and A. {Margulis} and M. {Margulis} and J. {Bradley} and A. {Kozak} and J. {Budge}},
title = {Next-Generation Software Defined Radar: First Results},
year = {2020},
month = {April},
pages = {749-754},
abstract = {ARTEMIS, Inc. has begun flight testing a new radar system called the SlimSDR (for Slim, Software Defined Radar). This successor to the ARTEMIS SlimSAR began test flights in September 2019. Like the SlimSAR, it is a compact radar system, but provides additional capabilities and flexibility. As a software defined radar, the SlimSDR is modular, multi-frequency, and applicable to multiple applications. This paper details the design and development process of the SlimSDR and shows initial results from the first flight tests of the system.},
issn = {2640-7736},

}


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This collection of SAR literature is far from being complete.
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