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An automatic change detection approach for rapid flood mapping in Sentinel-1 data

Li, Yu and Martinis, Sandro and Plank, Simon Manuel and Ludwig, Ralf (2018) An automatic change detection approach for rapid flood mapping in Sentinel-1 data. International Journal of Applied Earth Observation and Geoinformation, 73, pp. 123-135. Elsevier. doi: 10.1016/j.jag.2018.05.023. ISSN 1569-8432.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0303243418302782


In this paper, a two-step automatic change detection chain for rapid flood mapping based on Sentinel-1 Synthetic Aperture Radar (SAR) data is presented. First, a reference image is selected from a set of potential image candidates via a Jensen-Shannon (JS) divergence-based index. Second, saliency detection is applied on log-ratio data to derive the prior probabilities of changed and unchanged classes for initializing the following expectation-maximization (EM) based generalized Gaussian mixture model (GGMM). The saliency-guided GGMM is capable of capturing the primary pixel-based change information and handling highly imbalanced datasets. A fully-connected conditional random field (FCRF) model, which takes long-range pairwise potential connections into account, is integrated to remove the ambiguities of the saliency-guided GGMM and to achieve the final change map. The whole process chain is automatic with an efficient computation. The proposed approach was validated on flood events at the Evros River, Greece and the Wharfe River and Ouse River in York, United Kingdom. Kappa coefficients (k) of 0.9238 and 0.8682 were obtained respectively. The sensitivity analysis underlines the robustness of the proposed approach for rapid flood mapping.

Item URL in elib:https://elib.dlr.de/121380/
Document Type:Article
Title:An automatic change detection approach for rapid flood mapping in Sentinel-1 data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Li, Yuyu.li (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Plank, Simon ManuelSimon.Plank (at) dlr.dehttps://orcid.org/0000-0002-5793-052X
Ludwig, RalfDepartment of Geography, Ludwig-MaximiliansUniversity MuenchenUNSPECIFIED
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.jag.2018.05.023
Page Range:pp. 123-135
Keywords:Floods; Change detection; Saliency detection; Generalized Gaussian mixture model; Fully-connected conditional random field
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Martinis, Sandro
Deposited On:23 Aug 2018 08:48
Last Modified:16 Aug 2021 14:19

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