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Urban flood mapping using SAR intensity and interferometric coherence via Bayesian Network Fusion

Li, Yu und Martinis, Sandro und Wieland, Marc und Schlaffer, Stefan und Natsuaki, Ryo (2019) Urban flood mapping using SAR intensity and interferometric coherence via Bayesian Network Fusion. Remote Sensing, 11 (2231), Seiten 1-22. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs11192231. ISSN 2072-4292.

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Offizielle URL: https://www.mdpi.com/2072-4292/11/19/2231/pdf

Kurzfassung

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

elib-URL des Eintrags:https://elib.dlr.de/129470/
Dokumentart:Zeitschriftenbeitrag
Titel:Urban flood mapping using SAR intensity and interferometric coherence via Bayesian Network Fusion
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Li, Yuyu.li (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361XNICHT SPEZIFIZIERT
Wieland, Marcmarc.wieland (at) dlr.dehttps://orcid.org/0000-0002-1155-723XNICHT SPEZIFIZIERT
Schlaffer, Stefanstefan.schlaffer (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Natsuaki, Ryonatsuaki (at) ee.t.u-tokyo.ac.jpNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2019
Erschienen in:Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:11
DOI:10.3390/rs11192231
Seitenbereich:Seiten 1-22
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:veröffentlicht
Stichwörter:Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.
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 - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Institut für Hochfrequenztechnik und Radarsysteme > SAR-Technologie
Hinterlegt von: Martinis, Sandro
Hinterlegt am:08 Okt 2019 09:50
Letzte Änderung:03 Nov 2023 10:04

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