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Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images

Dumitru, Corneliu and Cui, Shiyong and Faur, Daniela and Datcu, Mihai (2015) Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (1), pp. 114-129. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2014.2320777 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6819782


In this paper, we present data analytics for a quantitative analysis in a rapid mapping scenario applied for damage assessment of the 2013 floods in Germany and the 2011 tsunami in Japan. These scenarios are created using pre- and postdisaster TerraSAR-X images and a semi-automated processing chain. All our dataset is tiled into patches and Gabor filters are applied as a primitive feature extraction method to each patch separately. A support vector machine with relevance feedback is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our investigation, nondamaged and damaged categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. The classifier supports rapid mapping scenarios (e.g., floods in Germany and tsunami in Japan) and interactive mapping generation. The quantitative damages can be assessed by: 1) flooded agricultural areas (21.66% in the case of floods in Germany and 4.15% in the case of tsunami in Japan) and destroyed aquaculture (2.33% in the case of tsunami in Japan); 2) destroyed transportation infrastructures, such as airport (50% in case tsunami in Japan), bridges, and roads.; and 3) debris that appears in postdisaster images (3.81% in the case of tsunami after the aquaculture was destroyed). The first analysis envisages the floods of Elbe river in June 2013: 30% of the investigated area, about ${bf 179}nbsphbox{bf km}^{bf 2}$ , including agricultural land, forest, river, and some residential and industrial areas close to the river, was covered by water. The second analysis, considering an area of ${bf 59}nbsphbox{bf km}^{bf 2}$ affected by the tsunami, led us to conclude that 3 months after the tsunami, some of the categories returned to previous functionality—the airport, othe- s return to partial functionality such as isolated residents, and some were totally destroyed—the aquaculture. The flooded area was about ${bf 59}nbsphbox{bf km}^{bf 2}$ . The proposed approach goes beyond a simple annotation of the data and provides an intermediate product in order to produce a relevant visual analytics representation of the data. This makes it easier to compare datasets and different quantitative findings in a meaningful manner, accessible both to experts and regular users. Our paper presents an interactive and automatic, fast processing method applicable to large and complex datasets (such as image time series). In addition to enhancing the information content extraction (number of identified categories), this approach enables the discovery and analysis of these categories. The novelty of this paper resides in that this is the first time data analytics have been run on a large dataset and for different scenarios using a semi-automated processing chain.

Item URL in elib:https://elib.dlr.de/93680/
Document Type:Article
Additional Information:Article#: 2320777
Title:Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dumitru, Corneliucorneliu.dumitru (at) dlr.deUNSPECIFIED
Cui, Shiyongshiyong.cui (at) dlr.deUNSPECIFIED
Faur, DanielaUniversity Politehnica of Bucharest, Bucharest, RomaniaUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:January 2015
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/JSTARS.2014.2320777
Page Range:pp. 114-129
Chanussot, Jocelynjocelyn.chanussot@gipsa-lab.grenoble-inp.fr
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Annotation, TerraSAR-X, data analytics, disaster, flooding, rapid mapping, scenario, taxonomy, tsunami
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 hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited On:17 Dec 2014 09:20
Last Modified:08 Mar 2018 18:31

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