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Gokon, Hideomi und Post, Joachim und Stein, Enrico und Martinis, Sandro und Twele, André und Mück, Matthias und Koshimura, Shunichi (2013) Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data. Journal of Japan Society of Civil Engineers, Ser. B2: Coastal Engineering, 69 (2), Seiten 1441-1445. Japan Society of Civil Engineers. doi: 10.2208/kaigan.69.I_1441. ISSN 1884-2399.
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Offizielle URL: https://www.jstage.jst.go.jp/article/kaigan/69/2/69_I_1441/_article
Kurzfassung
A method for rapid detection of tsunami devastated areas using multi-temporal TerraSAR-X data is proposed. To develop the method, machine learning algorithm, a branch of artificial intelligence (AI), is applied. We focus on the multiple bounce reflection which is a specific feature on Synthetic Aperture Radar (SAR) data to estimate building devastated areas. Finally, classifiers which enable automated classifications of damage patterns into predicted damage classes were built. The evaluation of the model was conducted through cross-validation and the best accuracy was obtained as 89.2 %.
| elib-URL des Eintrags: | https://elib.dlr.de/99679/ | ||||||||||||||||||||||||||||||||
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| Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
| Titel: | Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data | ||||||||||||||||||||||||||||||||
| Autoren: |
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| Datum: | 2013 | ||||||||||||||||||||||||||||||||
| Erschienen in: | Journal of Japan Society of Civil Engineers, Ser. B2: Coastal Engineering | ||||||||||||||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
| Open Access: | Ja | ||||||||||||||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||||||||||||||||||
| Band: | 69 | ||||||||||||||||||||||||||||||||
| DOI: | 10.2208/kaigan.69.I_1441 | ||||||||||||||||||||||||||||||||
| Seitenbereich: | Seiten 1441-1445 | ||||||||||||||||||||||||||||||||
| Verlag: | Japan Society of Civil Engineers | ||||||||||||||||||||||||||||||||
| ISSN: | 1884-2399 | ||||||||||||||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
| Stichwörter: | remote sensing, synthetic aperture radar, tsunami, building damage, machine learning | ||||||||||||||||||||||||||||||||
| 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 Zivile Kriseninformation und Georisiken (alt) | ||||||||||||||||||||||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
| Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||||||||||
| Hinterlegt von: | Twele, Andre | ||||||||||||||||||||||||||||||||
| Hinterlegt am: | 03 Dez 2015 09:40 | ||||||||||||||||||||||||||||||||
| Letzte Änderung: | 31 Jul 2019 19:56 |
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