Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

Détails

ID Serval
serval:BIB_4D63288586F8
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest
Périodique
IEEE Journal of Selected Topics in Applied Earth Observation
Auteur(s)
Longbotham N., Pacifici F., Glenn T., Zare A., Volpi M., Tuia D., Christophe E., Michel J., Inglada J., Chanussot J., Du Q.
ISSN-L
1939-1404
Statut éditorial
Publié
Date de publication
2012
Peer-reviewed
Oui
Volume
9
Pages
331-342
Langue
anglais
Notes
Longbotham2012
Résumé
The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical
Committee of the IEEE Geoscience and Remote Sensing Society was focused
on the detection of flooded areas using multi-temporal and multi-modal
images. Both high spatial resolution optical and synthetic aperture
radar data were provided. The goal was not only to identify the best
algorithms (in terms of accuracy), but also to investigate the further
improvement derived from decision fusion. This paper presents the
four awarded algorithms and the conclusions of the contest, investigating
both supervised and unsupervised methods and the use of multi-modal
data for flood detection. Interestingly, a simple unsupervised change
detection method provided similar accuracy as supervised approaches,
and a digital elevation model-based predictive method yielded a comparable
projected change detection map without using post-event data.
Création de la notice
25/11/2013 17:18
Dernière modification de la notice
20/08/2019 14:02
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