Learning spatial filters for multispectral image segmentation
Détails
ID Serval
serval:BIB_561A1E176AAD
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Learning spatial filters for multispectral image segmentation
Périodique
IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Grenoble, France
ISSN
978-1-4244-7876-7
ISSN-L
1551-2541
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Pages
41-46
Langue
anglais
Résumé
We present a novel filtering method for multispectral satellite image
classification. The proposed method learns a set of spatial filters
that maximize class separability of binary support vector machine
(SVM) through a gradient descent approach. Regularization issues
are discussed in detail and a Frobenius-norm regularization is proposed
to efficiently exclude uninformative filters coefficients. Experiments
carried out on multiclass one-against-all classification and target
detection show the capabilities of the learned spatial filters.
classification. The proposed method learns a set of spatial filters
that maximize class separability of binary support vector machine
(SVM) through a gradient descent approach. Regularization issues
are discussed in detail and a Frobenius-norm regularization is proposed
to efficiently exclude uninformative filters coefficients. Experiments
carried out on multiclass one-against-all classification and target
detection show the capabilities of the learned spatial filters.
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
20/08/2019 15:10
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