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dc.contributor.authorBai, Yanbingen_US
dc.contributor.authorGao, Changen_US
dc.contributor.authorSingh, Sameeren_US
dc.contributor.authorKoch, Magalyen_US
dc.contributor.authorAdriano, Brunoen_US
dc.contributor.authorMas, Ericken_US
dc.contributor.authorKoshimura, Shunichien_US
dc.date.accessioned2018-07-05T14:45:54Z
dc.date.available2018-07-05T14:45:54Z
dc.date.issued2018-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000419088600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationYanbing Bai, Chang Gao, Sameer Singh, Magaly Koch, Bruno Adriano, Erick Mas, Shunichi Koshimura. 2018. "A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks." IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v. 15, issue 1, pp. 43 - 47 (5).
dc.identifier.issn1545-598X
dc.identifier.issn1558-0571
dc.identifier.urihttps://hdl.handle.net/2144/29743
dc.description.abstractNear real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.en_US
dc.description.sponsorshipThis work was supported in part by JST CREST, Japan, under Grant JPMJCR1411 and in part by the China Scholarship Council. (JPMJCR1411 - JST CREST, Japan; China Scholarship Council)en_US
dc.format.extentp. 43 - 47en_US
dc.languageEnglish
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
dc.subjectDeep neural networksen_US
dc.subjectSARen_US
dc.subjectScience & technologyen_US
dc.subjectPhysical sciencesen_US
dc.subjectGeochemistry & geophysicsen_US
dc.subjectEngineering, electrical & electronicen_US
dc.subjectRemote sensingen_US
dc.subjectImaging science & photographic technologyen_US
dc.subjectFrameworken_US
dc.subjectPost-event TerraSAR-X imageryen_US
dc.subjectRapiden_US
dc.subjectRegional tsunami damage recognitionen_US
dc.subjectGeological & geomatics engineeringen_US
dc.titleA framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/LGRS.2017.2772349
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-6186-1619 (Koch, Magaly)


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