Multisensor change detection with nonlinear canonical correlation
Détails
ID Serval
serval:BIB_59B8DB782153
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
Multisensor change detection with nonlinear canonical correlation
Titre de la conférence
IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia
Statut éditorial
Publié
Date de publication
2013
Langue
anglais
Notes
Volpi2013d
Résumé
The analysis of multi-modal and multi-sensor images is nowadays of
paramount importance for Earth Observation (EO) applications. There
exist a variety of methods that aim at fusing the different sources
of information to obtain a compact representation of such datasets.
However, for change detection existing methods are often unable to
deal with heterogeneous image sources and very few consider possible
nonlinearities in the data. Additionally, the availability of labeled
information is very limited in change detection applications. For
these reasons, we present the use of a semi-supervised kernel-based
feature extraction technique. It incorporates a manifold regularization
accounting for the geometric distribution and jointly addressing
the small sample problem. An exhaustive example using Landsat 5 data
illustrates the potential of the method for multi-sensor change detection.
paramount importance for Earth Observation (EO) applications. There
exist a variety of methods that aim at fusing the different sources
of information to obtain a compact representation of such datasets.
However, for change detection existing methods are often unable to
deal with heterogeneous image sources and very few consider possible
nonlinearities in the data. Additionally, the availability of labeled
information is very limited in change detection applications. For
these reasons, we present the use of a semi-supervised kernel-based
feature extraction technique. It incorporates a manifold regularization
accounting for the geometric distribution and jointly addressing
the small sample problem. An exhaustive example using Landsat 5 data
illustrates the potential of the method for multi-sensor change detection.
Mots-clé
Change detection, Multi-sensor, Multimodal, Feature extraction, Radiometric, normalization, LTS5
Création de la notice
25/11/2013 17:23
Dernière modification de la notice
20/08/2019 14:13
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