BACK TO INDEX BACK TO OTHMAR FREY'S HOMEPAGE Publications of year 2023 Articles in journal or book chapters
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| Abstract: | Riverbank erosion occurs along many of the Earth's river systems, affecting riverine populations by destroying agricultural land and housing. In this study, we detected past events of riverbank erosion along the Jamuna River in Bangladesh using time series of Sentinel-1 satellite radar imagery, ground-range-detected (GRD) data with a 12 d revisit cycle, available in the Google Earth Engine (GEE). Eroded land is detected by performing a land cover classification and by detecting land cover changes from vegetated areas before the monsoon to sand or water after the monsoon. Further, settlements are detected as persistent scatterers and classified as eroded if they are located on eroded land. We found that with Sentinel-1 data, erosion locations can be determined already 1 month after the end of the monsoon and hence potentially earlier than using optical satellite images which depend on cloud-free daylight conditions. Further, we developed an interactive GEE-based online tool allowing the user to explore where riverbank erosion has destroyed land and settlements along the Jamuna in five monsoon seasons (2015-2019). The source code of our implementation is publicly available, providing the opportunity to reproduce the results, to adapt the algorithm and to transfer our results to assess riverbank erosion in other geographical settings. |
@Article{freihardtFreyNHESS2023RiverbankErosionBangladeshSentinel1GoogleEarthEngine,
author = {Freihardt, Jan and Frey, Othmar},
journal = {Nat. Hazards Earth Syst. Sci.,},
title = {Assessing riverbank erosion in {Bangladesh} using time series of {Sentinel-1} radar imagery in the {Google} {Earth} {Engine}},
year = {2023},
pages = {751--770},
volume = {23},
abstract = {Riverbank erosion occurs along many of the Earth's river systems, affecting riverine populations by destroying agricultural land and housing. In this study, we detected past events of riverbank erosion along the Jamuna River in Bangladesh using time series of Sentinel-1 satellite radar imagery, ground-range-detected (GRD) data with a 12 d revisit cycle, available in the Google Earth Engine (GEE). Eroded land is detected by performing a land cover classification and by detecting land cover changes from vegetated areas before the monsoon to sand or water after the monsoon. Further, settlements are detected as persistent scatterers and classified as eroded if they are located on eroded land. We found that with Sentinel-1 data, erosion locations can be determined already 1 month after the end of the monsoon and hence potentially earlier than using optical satellite images which depend on cloud-free daylight conditions. Further, we developed an interactive GEE-based online tool allowing the user to explore where riverbank erosion has destroyed land and settlements along the Jamuna in five monsoon seasons (2015-2019). The source code of our implementation is publicly available, providing the opportunity to reproduce the results, to adapt the algorithm and to transfer our results to assess riverbank erosion in other geographical settings.},
doi = {10.5194/nhess-23-751-2023},
file = {:freihardtFreyNHESS2023RiverbankErosionBangladeshSentinel1GoogleEarthEngine.pdf:PDF},
issue = {2},
keywords = {SAR Processing, Sentinel-1, time series, Google Earth Engine, GEE, Jamuna, Jamuna River, Bangladesh, erosion, riverbank erosion, classification, natural hazard, geohazard},
owner = {ofrey},
url = {https://nhess.copernicus.org/articles/23/751/2023/},
}
Keyword(s): Synthetic aperture radar,
Radar,
Radar imaging,
Radar polarimetry,
Radar antennas,
Automotive engineering,
Navigation,
SAR,
automotive,
MIMO,
autofocus,
motion compensation.
| Abstract: | With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-called autofocus algorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocus requirements in typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets' localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration. |
@Article{manzoniEtalTIT2023SMotionEstimationAndCompensationInAutomotiveMIMOSAR,
author = {Manzoni, Marco and Tagliaferri, Dario and Rizzi, Marco and Tebaldini, Stefano and Guarnieri, Andrea Virgilio Monti and Prati, Claudio Maria and Nicoli, Monica and Russo, Ivan and Duque, Sergi and Mazzucco, Christian and Spagnolini, Umberto},
journal = {IEEE Transactions on Intelligent Transportation Systems},
title = {Motion Estimation and Compensation in Automotive MIMO SAR},
year = {2023},
issn = {1558-0016},
month = {Feb},
number = {2},
pages = {1756-1772},
volume = {24},
abstract = {With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-called autofocus algorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocus requirements in typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets' localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.},
doi = {10.1109/TITS.2022.3219542},
file = {:manzoniEtalTIT2023SMotionEstimationAndCompensationInAutomotiveMIMOSAR.pdf:PDF},
keywords = {Synthetic aperture radar;Radar;Radar imaging;Radar polarimetry;Radar antennas;Automotive engineering;Navigation;SAR;automotive;MIMO;autofocus;motion compensation},
owner = {ofrey},
}
Keyword(s): Radar imaging,
Radar,
Automotive engineering,
MIMO communication,
Automobiles,
Radar antennas,
Geometry,
Automotive,
double bounce,
ghost targets,
multipath,
multiple-input multiple-output (MIMO),
radar,
synthetic aperture radar (SAR).
| Abstract: | This article discusses the effect of multipath in automotive radar imaging under different sensor configurations. The study is motivated by the fact that radar technologies are becoming indispensable in the automotive scenario. Many applications such as collision avoidance systems, assisted parking, and driving assistance systems take advantage of radar technologies to accomplish their task. However, one of the main concerns about automotive radars is the possibility of detecting false targets due to multiple signal reflections. In this article, we show how different sensor layouts experience multipath differently. In particular, we demonstrate that with multiple-input multiple-output (MIMO) radars, what really matters is the physical positions of the transmitting and receiving antennas. The monostatic/bistatic equivalent configurations cannot be used to design a system and to simulate an acquisition in the presence of a multipath. We also demonstrate how vehicle-based MIMO-synthetic aperture radar (MIMO-SAR) imaging can generate a bi-dimensional aperture which significantly reduces multipath effects in the focused image, avoiding the detection of false targets. All the theoretical analyses are supported by several simulations where different sensor layouts are tested, and the capability of MIMO-SAR to reject multipath is validated. |
@Article{manzoniTebaldiniMontiGuarnieriPratiTGRS2023MultipathInAutomoticeMIMOSARImaging,
author = {Manzoni, Marco and Tebaldini, Stefano and Monti-Guarnieri, Andrea Virgilio and Prati, Claudio Maria},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Multipath in Automotive MIMO SAR Imaging},
year = {2023},
issn = {1558-0644},
pages = {1-12},
volume = {61},
abstract = {This article discusses the effect of multipath in automotive radar imaging under different sensor configurations. The study is motivated by the fact that radar technologies are becoming indispensable in the automotive scenario. Many applications such as collision avoidance systems, assisted parking, and driving assistance systems take advantage of radar technologies to accomplish their task. However, one of the main concerns about automotive radars is the possibility of detecting false targets due to multiple signal reflections. In this article, we show how different sensor layouts experience multipath differently. In particular, we demonstrate that with multiple-input multiple-output (MIMO) radars, what really matters is the physical positions of the transmitting and receiving antennas. The monostatic/bistatic equivalent configurations cannot be used to design a system and to simulate an acquisition in the presence of a multipath. We also demonstrate how vehicle-based MIMO-synthetic aperture radar (MIMO-SAR) imaging can generate a bi-dimensional aperture which significantly reduces multipath effects in the focused image, avoiding the detection of false targets. All the theoretical analyses are supported by several simulations where different sensor layouts are tested, and the capability of MIMO-SAR to reject multipath is validated.},
doi = {10.1109/TGRS.2023.3240705},
file = {:manzoniTebaldiniMontiGuarnieriPratiTGRS2023MultipathInAutomoticeMIMOSARImaging.pdf:PDF},
keywords = {Radar imaging;Radar;Automotive engineering;MIMO communication;Automobiles;Radar antennas;Geometry;Automotive;double bounce;ghost targets;multipath;multiple-input multiple-output (MIMO);radar;synthetic aperture radar (SAR)},
owner = {ofrey},
}
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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME
should NOT be mistaken for a complete bibliography of that author.
Last modified: Fri Feb 24 14:22:26 2023
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .
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