Learning spatial filters for multispectral image segmentation
Details
Serval ID
serval:BIB_561A1E176AAD
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Learning spatial filters for multispectral image segmentation
Journal
IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Grenoble, France
ISSN
978-1-4244-7876-7
ISSN-L
1551-2541
Publication state
Published
Issued date
2010
Peer-reviewed
Oui
Pages
41-46
Language
english
Abstract
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.
Create date
25/11/2013 18:18
Last modification date
20/08/2019 15:10
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