Unsupervised Change Detection via Hierarchical Support Vector Clustering
Détails
Etat: Public
Version: de l'auteur⸱e
ID Serval
serval:BIB_1FC08A9B0F04
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).
Collection
Publications
Institution
Titre
Unsupervised Change Detection via Hierarchical Support Vector Clustering
Titre de la conférence
ICPR 2012, Pattern Recognition Remote Sensing Workshop
Adresse
Tsukuba, Japan, November 11-15, 2012
Statut éditorial
Publié
Date de publication
2012
Langue
anglais
Résumé
When dealing with change detection problems, information
about the nature of the changes is often unavailable. In
this paper we propose a solution to perform unsupervised
change detection based on nonlinear support vector
clustering. We build a series of nested hierarchical
support vector clustering descriptions, select the
appropriate one using a cluster validity measure and
finally merge the clusters into two classes,
corresponding to changed and unchanged areas. Experiments
on two multispectral datasets confirm the power and
appropriateness of the proposed system.
about the nature of the changes is often unavailable. In
this paper we propose a solution to perform unsupervised
change detection based on nonlinear support vector
clustering. We build a series of nested hierarchical
support vector clustering descriptions, select the
appropriate one using a cluster validity measure and
finally merge the clusters into two classes,
corresponding to changed and unchanged areas. Experiments
on two multispectral datasets confirm the power and
appropriateness of the proposed system.
Mots-clé
Cluster Validity measure, merging system, remote, sensing, entire solution path, nested SVM, LTS5
Création de la notice
06/01/2014 22:07
Dernière modification de la notice
20/08/2019 13:55
Données d'usage
