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

Cao, Wenxi and Martinis, Sandro (2015) SAR-based change detection using hypothesis testing and Markov random field modelling. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7 (W3), pp. 783-790. The 36th International Symposium on Remote Sensing of Environment (ISRSE), 11.-25. Mai. 2015, Berlin, Germany. DOI: 10.5194/isprsarchives-XL-7-W3-783-2015 ISSN 1682-1750

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

Abstract

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.

Item URL in elib:https://elib.dlr.de/99004/
Document Type:Conference or Workshop Item (Speech)
Title:SAR-based change detection using hypothesis testing and Markov random field modelling
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cao, Wenxiwenxi.cao (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.deUNSPECIFIED
Date:2015
Journal or Publication Title:Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:XL-7
DOI :10.5194/isprsarchives-XL-7-W3-783-2015
Page Range:pp. 783-790
ISSN:1682-1750
Status:Published
Keywords:Three-Class Change Detection, Synthetic Aperture Radar (SAR), Post-Classification, Disaster Monitoring, Graph-Cut, Markov Random Field (MRF)
Event Title:The 36th International Symposium on Remote Sensing of Environment (ISRSE)
Event Location:Berlin, Germany
Event Type:international Conference
Event Dates:11.-25. Mai. 2015
Organizer:DLR
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 Zivile Kriseninformation und Georisiken (old), V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Cao, Wenxi
Deposited On:11 Nov 2015 15:05
Last Modified:31 Jul 2019 19:55

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