Wang, Yuanyuan und Zhu, Xiao Xiang (2017) Earth observation using SAR and social media images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seiten 95-103. IEEE Xplore. CVPR 2017 EarthVision Workshop, 2017-07-21, Honolulu, USA. doi: 10.1109/CVPRW.2017.202.
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Offizielle URL: https://www.semanticscholar.org/paper/Earth-Observation-Using-SAR-and-Social-Media-Image-/cddd758e6b542d08ba46155b03a1257f292b178c
Kurzfassung
Earth Observation (EO) is mostly carried out through centralized optical and synthetic aperture radar (SAR) missions. Despite the controlled quality of their products, such observation is restricted by the characteristics of the sensor platform, e.g. the revisit time. Over the last decade, the rapid development of social media has accumulated vast amount of online images. Despite their uncontrolled quality, the sheer volume may contain useful information that can complement the EO missions, especially the SAR missions. This paper presents a preliminary work of fusing social media and SAR images. They have distinct imaging geometries, which are nearly impossible to even coregister without a precise 3-D model. We describe a general approach to coregister them without using external 3-D model. We demonstrate that, one can obtain a new kind of 3-D city model that includes the optical texture for better scene understanding and the precise deformation retrieved from SAR interferometry.
elib-URL des Eintrags: | https://elib.dlr.de/113971/ | ||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag, Poster) | ||||||||||||
Titel: | Earth observation using SAR and social media images | ||||||||||||
Autoren: |
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Datum: | 2017 | ||||||||||||
Erschienen in: | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
DOI: | 10.1109/CVPRW.2017.202 | ||||||||||||
Seitenbereich: | Seiten 95-103 | ||||||||||||
Verlag: | IEEE Xplore | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | social media, SAR, InSAR, 3-D model, deformation, structure from motion, TomoSAR | ||||||||||||
Veranstaltungstitel: | CVPR 2017 EarthVision Workshop | ||||||||||||
Veranstaltungsort: | Honolulu, USA | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsdatum: | 21 Juli 2017 | ||||||||||||
Veranstalter : | IEEE/ISPRS | ||||||||||||
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: | Wang, Yuanyuan | ||||||||||||
Hinterlegt am: | 12 Sep 2017 12:54 | ||||||||||||
Letzte Änderung: | 24 Apr 2024 20:18 |
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