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Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs

Martinis, Sandro and Twele, André and Voigt, Stefan (2011) Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs. IEEE Transactions on Geoscience and Remote Sensing, 49 (1), pp. 251-263. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2010.2052816.

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5535084&tag=1


The near real-time provision of precise information about flood dynamics from synthetic aperture radar (SAR) data is an essential task in disaster management. A novel tile-based parametric thresholding approach under the generalized Gaussian assumption is applied on normalized change index data to automatically solve the three-class change detection problem in large-size images with small class a priori probabilities. The thresholding result is used for the initialization of a hybrid Markov model which integrates scale-dependent and spatiocontextual information into the labeling process by combining hierarchical with noncausal Markov image modeling. Hierarchical maximum a posteriori (HMAP) estimation using the Markov chains in scale, originally developed on quadtrees, is adapted to hierarchical irregular graphs. To reduce the computational effort of the iterative optimization process that is related to noncausalMarkovmodels, a Markov random field (MRF) approach is defined, which is applied on a restricted region of the lowest level of the graph, selected according to the HMAP labeling result. The experiments that were performed on a bitemporal TerraSAR-X StripMap data set from SouthWest England during and after a large-scale flooding in 2007 confirm the effectiveness of the proposed change detection method and show an increased classification accuracy of the hybrid MRF model in comparison to the sole application of the HMAP estimation. Additionally, the impact of the graph structure and the chosen model parameters on the labeling result as well as on the performance is discussed.

Item URL in elib:https://elib.dlr.de/66326/
Document Type:Article
Title:Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:January 2011
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 251-263
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Automatic thresholding, change detection, flood mapping, generalized Gaussian distribution, hierarchical maximum a posteriori (HMAP) estimation, irregular graph, Markov random field (MRF)
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben CHARTA & EO-Krisenlagezentrum (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Civil Crisis Information and Geo Risks
Deposited By: Martinis, Sandro
Deposited On:03 Feb 2011 19:30
Last Modified:08 Mar 2018 18:34

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