elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data

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

[img] PDF (Gokon et al. 2013)
2MB

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/
Document Type:Article
Title:Machine Learning Based Method for Detecting Tsunami Devastated Area Using TerraSAR-X Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Gokon, HideomiUNSPECIFIEDUNSPECIFIED
Post, JoachimUNSPECIFIEDUNSPECIFIED
Stein, Enricoenrico.stein (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.deUNSPECIFIED
Twele, Andréandre.twele (at) dlr.deUNSPECIFIED
Mück, Matthiasmatthias.mueck (at) dlr.deUNSPECIFIED
Koshimura, ShunichiUNSPECIFIEDUNSPECIFIED
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 - Erdbeobachtung
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

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.