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Gokon, Hideomi and Post, Joachim and Stein, Enrico and Martinis, Sandro and Twele, André and Mück, Matthias and 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), pp. 1441-1445. Japan Society of Civil Engineers. doi: 10.2208/kaigan.69.I_1441. ISSN 1884-2399.
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Official URL: https://www.jstage.jst.go.jp/article/kaigan/69/2/69_I_1441/_article
Abstract
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 %.
| Item URL in elib: | https://elib.dlr.de/99679/ | ||||||||||||||||||||||||||||||||
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| Document Type: | Article | ||||||||||||||||||||||||||||||||
| Title: | Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data | ||||||||||||||||||||||||||||||||
| Authors: |
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| Date: | 2013 | ||||||||||||||||||||||||||||||||
| Journal or Publication Title: | Journal of Japan Society of Civil Engineers, Ser. B2: Coastal Engineering | ||||||||||||||||||||||||||||||||
| Refereed publication: | Yes | ||||||||||||||||||||||||||||||||
| Open Access: | Yes | ||||||||||||||||||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||||||||||||||||||
| In SCOPUS: | No | ||||||||||||||||||||||||||||||||
| In ISI Web of Science: | No | ||||||||||||||||||||||||||||||||
| Volume: | 69 | ||||||||||||||||||||||||||||||||
| DOI: | 10.2208/kaigan.69.I_1441 | ||||||||||||||||||||||||||||||||
| Page Range: | pp. 1441-1445 | ||||||||||||||||||||||||||||||||
| Publisher: | Japan Society of Civil Engineers | ||||||||||||||||||||||||||||||||
| ISSN: | 1884-2399 | ||||||||||||||||||||||||||||||||
| Status: | Published | ||||||||||||||||||||||||||||||||
| Keywords: | remote sensing, synthetic aperture radar, tsunami, building damage, machine learning | ||||||||||||||||||||||||||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||||||||||
| HGF - Program: | Space | ||||||||||||||||||||||||||||||||
| HGF - Program Themes: | Earth Observation | ||||||||||||||||||||||||||||||||
| DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||||||||||||||
| DLR - Research theme (Project): | R - Vorhaben Zivile Kriseninformation und Georisiken (old) | ||||||||||||||||||||||||||||||||
| Location: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
| Institutes and Institutions: | German Remote Sensing Data Center > Geo Risks and Civil Security | ||||||||||||||||||||||||||||||||
| Deposited By: | Twele, Andre | ||||||||||||||||||||||||||||||||
| Deposited On: | 03 Dec 2015 09:40 | ||||||||||||||||||||||||||||||||
| Last Modified: | 31 Jul 2019 19:56 |
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