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

Martinis, Sandro und Twele, André und 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), Seiten 251-263. DOI: 10.1109/TGRS.2010.2052816.

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

Kurzfassung

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.

Dokumentart:Zeitschriftenbeitrag
Titel:Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs
Autoren:
AutorenInstitution oder E-Mail-Adresse der Autoren
Martinis, Sandrosandro.martinis@dlr.de
Twele, Andréandre.twele@dlr.de
Voigt, StefanStefan.Voigt@dlr.de
Datum:Januar 2011
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
In Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:49
DOI :10.1109/TGRS.2010.2052816
Seitenbereich:Seiten 251-263
Status:veröffentlicht
Stichwörter:Automatic thresholding, change detection, flood mapping, generalized Gaussian distribution, hierarchical maximum a posteriori (HMAP) estimation, irregular graph, Markov random field (MRF)
HGF - Forschungsbereich:Verkehr und Weltraum (alt)
HGF - Programm:Weltraum (alt)
HGF - Programmthema:W EO - Erdbeobachtung
DLR - Schwerpunkt:Weltraum
DLR - Forschungsgebiet:W EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):W - Vorhaben CHARTA & EO-Krisenlagezentrum (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Zivile Kriseninformation und Georisiken
Hinterlegt von: Sandro Martinis
Hinterlegt am:03 Feb 2011 19:30
Letzte Änderung:19 Apr 2013 13:49

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