Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study
Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few me...
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ftdlr:oai:elib.dlr.de:204480 2024-09-09T19:42:28+00:00 Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study Mansour, Islam Fischer, Georg Hänsch, Ronny Hajnsek, Irena Papathanassiou, Konstantinos 2024 application/pdf https://elib.dlr.de/204480/ https://elib.dlr.de/204480/1/20240525022445_702775_5799.pdf en eng https://elib.dlr.de/204480/1/20240525022445_702775_5799.pdf Mansour, Islam und Fischer, Georg und Hänsch, Ronny und Hajnsek, Irena und Papathanassiou, Konstantinos (2024) Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study. In: International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2024-07-07 - 2024-07-12, Athens, Greece. Radarkonzepte Institut für Hochfrequenztechnik und Radarsysteme SAR-Technologie Konferenzbeitrag PeerReviewed 2024 ftdlr 2024-06-18T23:55:52Z Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band. Our method addresses the limitations of traditional physical modeling techniques by parameterizing the vertical structure function using a ML model. This approach combines machine learning as input for the physical model. The results demonstrate the improvements in correcting elevation biases, thus increasing the accuracy of X-band InSAR DEMs over Greenland. This advancement has the potential for more precise elevation estimation and ice-sheet monitoring. Conference Object Greenland Ice Sheet German Aerospace Center: elib - DLR electronic library Greenland |
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Open Polar |
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German Aerospace Center: elib - DLR electronic library |
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language |
English |
topic |
Radarkonzepte Institut für Hochfrequenztechnik und Radarsysteme SAR-Technologie |
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Radarkonzepte Institut für Hochfrequenztechnik und Radarsysteme SAR-Technologie Mansour, Islam Fischer, Georg Hänsch, Ronny Hajnsek, Irena Papathanassiou, Konstantinos Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
topic_facet |
Radarkonzepte Institut für Hochfrequenztechnik und Radarsysteme SAR-Technologie |
description |
Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band. Our method addresses the limitations of traditional physical modeling techniques by parameterizing the vertical structure function using a ML model. This approach combines machine learning as input for the physical model. The results demonstrate the improvements in correcting elevation biases, thus increasing the accuracy of X-band InSAR DEMs over Greenland. This advancement has the potential for more precise elevation estimation and ice-sheet monitoring. |
format |
Conference Object |
author |
Mansour, Islam Fischer, Georg Hänsch, Ronny Hajnsek, Irena Papathanassiou, Konstantinos |
author_facet |
Mansour, Islam Fischer, Georg Hänsch, Ronny Hajnsek, Irena Papathanassiou, Konstantinos |
author_sort |
Mansour, Islam |
title |
Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
title_short |
Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
title_full |
Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
title_fullStr |
Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
title_full_unstemmed |
Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study |
title_sort |
correction of the penetration bias for insar dem via synergetic ai-physical modeling: a greenland case study |
publishDate |
2024 |
url |
https://elib.dlr.de/204480/ https://elib.dlr.de/204480/1/20240525022445_702775_5799.pdf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_relation |
https://elib.dlr.de/204480/1/20240525022445_702775_5799.pdf Mansour, Islam und Fischer, Georg und Hänsch, Ronny und Hajnsek, Irena und Papathanassiou, Konstantinos (2024) Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study. In: International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2024-07-07 - 2024-07-12, Athens, Greece. |
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1809911726148157440 |