Multisource Clustering of Remote Sensing Images With Entropy-Based Dempster-Shafer Fusion
Détails
Etat: Public
Version: de l'auteur⸱e
ID Serval
serval:BIB_97A0622EA0C3
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Sous-type
Abstract (résumé de présentation): article court qui reprend les éléments essentiels présentés à l'occasion d'une conférence scientifique dans un poster ou lors d'une intervention orale.
Collection
Publications
Institution
Titre
Multisource Clustering of Remote Sensing Images With Entropy-Based Dempster-Shafer Fusion
Titre de la conférence
EUSIPCO 2013, European Signal Processing Conference
Adresse
Marrakech, Morocco, September 9-13, 2013
Statut éditorial
Publié
Date de publication
2013
Langue
anglais
Résumé
In this paper, we propose a strategy for fusing
clustering maps obtained with different remote sensing
sources. Dempster- Shafer (DS) Theory is a powerful
fusion method that allows to combine classifications from
different sources and handles ignorance, imprecision and
conflict between them. To do so, it attributes evidences
(weights) to different hypothesis representing single or
unions of classes. We introduce a fully unsupervised
evidence assignment strategy exploiting the entropy among
cluster memberships. Ambiguous pixels get stronger
evidences for union of classes to better represent the
uncertainty among them. On two multisource experiments,
the proposed Entropy-based Dempster-Shafer (EDS) performs
best along the different fusion methods with VHR images,
when the single class accuracies from each source are
complementary and one of the sources shows low overall
accuracy.
clustering maps obtained with different remote sensing
sources. Dempster- Shafer (DS) Theory is a powerful
fusion method that allows to combine classifications from
different sources and handles ignorance, imprecision and
conflict between them. To do so, it attributes evidences
(weights) to different hypothesis representing single or
unions of classes. We introduce a fully unsupervised
evidence assignment strategy exploiting the entropy among
cluster memberships. Ambiguous pixels get stronger
evidences for union of classes to better represent the
uncertainty among them. On two multisource experiments,
the proposed Entropy-based Dempster-Shafer (EDS) performs
best along the different fusion methods with VHR images,
when the single class accuracies from each source are
complementary and one of the sources shows low overall
accuracy.
Mots-clé
Dempster-Shafer, Multi-sensor, multisource fusion, unsupervised, entropy, fuzzy c-means, remote sensing, LTS5
Création de la notice
06/01/2014 21:46
Dernière modification de la notice
20/08/2019 15:59
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