Shi, Yilei und Bamler, Richard und Wang, Yuanyuan und Zhu, Xiao Xiang (2021) Generation of large scale 3-D city models using InSAR and optical data. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 2931-2934. IEEE. IGARSS 2021, 2021-07-11 - 2021-07-16, Brussels, Belgium. doi: 10.1109/IGARSS47720.2021.9553691. ISBN 978-1-6654-0369-6. ISSN 2153-7003.
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Offizielle URL: https://ieeexplore.ieee.org/document/9553691
Kurzfassung
Interferometric synthetic aperture radar (InSAR) techniquesare powerful tool for reconstructing the 3-D position of scat-terers, especially for the urban areas. Since the estimationaccuracy depends on the inverse of number of interferogramsand signal-to-noise ratio (SNR), it is necessary to use as manyas possible interferograms in order to achieve more accurateresult. However, the number of interferograms of TanDEM-Xdata is generally limited for most areas. Therefore, in orderto maintain the estimation accuracy, one feasible way is toincrease the SNR. In this work, we proposed a novel frame-work, which integrates the non-local procedure into SARtomography inversion and combines the robust estimation.A large-scale demonstration has been carried out with fiveTanDEM-X bistatic data, which covers the entire city of Mu-nich, Germany. Quantitative evaluation of the reconstructedresult with the LiDAR reference exhibits the standard devi-ation of the height difference is within two meters, whichimplies the proposed framework has great potential for highquality large-scale 3-D urban modeling.
elib-URL des Eintrags: | https://elib.dlr.de/146249/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Zusätzliche Informationen: | So2Sat | ||||||||||||||||||||
Titel: | Generation of large scale 3-D city models using InSAR and optical data | ||||||||||||||||||||
Autoren: |
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Datum: | 12 Oktober 2021 | ||||||||||||||||||||
Erschienen in: | International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
DOI: | 10.1109/IGARSS47720.2021.9553691 | ||||||||||||||||||||
Seitenbereich: | Seiten 2931-2934 | ||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||
ISSN: | 2153-7003 | ||||||||||||||||||||
ISBN: | 978-1-6654-0369-6 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | 3-D urban models, InSAR, TomoSAR,TanDEM-X, global mapping | ||||||||||||||||||||
Veranstaltungstitel: | IGARSS 2021 | ||||||||||||||||||||
Veranstaltungsort: | Brussels, Belgium | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 11 Juli 2021 | ||||||||||||||||||||
Veranstaltungsende: | 16 Juli 2021 | ||||||||||||||||||||
Veranstalter : | IEEE | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science Institut für Methodik der Fernerkundung > Leitungsbereich MF | ||||||||||||||||||||
Hinterlegt von: | Wang, Yuanyuan | ||||||||||||||||||||
Hinterlegt am: | 29 Nov 2021 08:13 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:45 |
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