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SAR-based change detection using hypothesis testing and Markov random field modelling

Cao, Wenxi und Martinis, Sandro (2015) SAR-based change detection using hypothesis testing and Markov random field modelling. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XL-7 (W3), Seiten 783-790. The 36th International Symposium on Remote Sensing of Environment (ISRSE), 2015-05-11 - 2015-05-25, Berlin, Germany. doi: 10.5194/isprsarchives-XL-7-W3-783-2015. ISSN 1682-1750.

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Offizielle URL: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/783/2015/isprsarchives-XL-7-W3-783-2015.html

Kurzfassung

The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

elib-URL des Eintrags:https://elib.dlr.de/99004/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:SAR-based change detection using hypothesis testing and Markov random field modelling
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Cao, Wenxiwenxi.cao (at) dlr.dehttps://orcid.org/0000-0001-9567-3053NICHT SPEZIFIZIERT
Martinis, Sandrosandro.martinis (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2015
Erschienen in:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
Band:XL-7
DOI:10.5194/isprsarchives-XL-7-W3-783-2015
Seitenbereich:Seiten 783-790
ISSN:1682-1750
Status:veröffentlicht
Stichwörter:Three-Class Change Detection, Synthetic Aperture Radar (SAR), Post-Classification, Disaster Monitoring, Graph-Cut, Markov Random Field (MRF)
Veranstaltungstitel:The 36th International Symposium on Remote Sensing of Environment (ISRSE)
Veranstaltungsort:Berlin, Germany
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:11 Mai 2015
Veranstaltungsende:25 Mai 2015
Veranstalter :DLR
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Zivile Kriseninformation und Georisiken (alt), V - Vabene++ (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Cao, Wenxi
Hinterlegt am:11 Nov 2015 15:05
Letzte Änderung:24 Apr 2024 20:04

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