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Automatic detection and reconstruction of 2D/3D building shapes from spaceborne TomoSAR point clouds

Shahzad, Muhammad und Zhu, Xiao Xiang (2016) Automatic detection and reconstruction of 2D/3D building shapes from spaceborne TomoSAR point clouds. IEEE Transactions on Geoscience and Remote Sensing, 54 (3), Seiten 1292-1310. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2015.2477429. ISSN 0196-2892.

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Offizielle URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7283615

Kurzfassung

Modern spaceborne SAR sensors such as TerraSAR-X/ TanDEM-X and COSMO-SkyMed can deliver very high resolution (VHR) data beyond the inherent spatial scales of buildings. Processing this VHR data with advanced interferometric techniques such as SAR tomography (TomoSAR) allows generation of 4D point clouds; containing not only the 3D positions of the scatterer location but also estimates of seasonal/temporal deformation on the scale of centimeters or even millimeters; making them very attractive for generating dynamic city models from space. Motivated by these chances, the authors have earlier proposed approaches that demonstrated first attempts towards reconstruction of building façades from this class of data. The approaches work well when high density of façade points exist and full shape of the building could be reconstructed if data is available from multiple views e.g., from both ascending and descending orbits. However, there are cases when no or only few façade points are available. This happens usually for lower height buildings and renders detection of façade points/regions very challenging. Moreover, problems related to the visibility of façades mainly pointing towards the azimuth direction can also cause difficulties in deriving the complete structure of individual buildings. These problems motivated us to reconstruct full 2D/3D shape of buildings via exploitation of roof points. In this paper, we present a novel and complete data driven framework for automatic (parametric) reconstruction of 2D/3D building shapes (or footprints) using unstructured TomoSAR points clouds generated particularly from one viewing angle only. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from single viewing angle TerraSAR-X high-resolution spotlight data stacks covering two different test areas with one containing simple moderate sized buildings in the city of Las Vegas, USA and the other containing relatively complex building structures in the city of Berlin, Germany.

elib-URL des Eintrags:https://elib.dlr.de/96303/
Dokumentart:Zeitschriftenbeitrag
Titel:Automatic detection and reconstruction of 2D/3D building shapes from spaceborne TomoSAR point clouds
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Shahzad, Muhammadmuhammad.shahzad (at) bv.tum.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zhu, Xiao XiangDLR-IMF/TUM-LMFNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2016
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:54
DOI:10.1109/TGRS.2015.2477429
Seitenbereich:Seiten 1292-1310
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Plaza, Antonio J.University of Extremadura, SpainNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:veröffentlicht
Stichwörter:Building reconstruction, dynamic city models, TerraSAR-X, tomographic SAR (TomoSAR) inversion, 4D point cloud, building footprint, clustering.
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 - Vorhaben hochauflösende Fernerkundungsverfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > SAR-Signalverarbeitung
Hinterlegt von: Shahzad, Muhammad
Hinterlegt am:26 Mai 2015 13:01
Letzte Änderung:06 Nov 2023 08:55

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