Learning spatial filters for multispectral image segmentation


Autoria(s): Tuia D.; Camps-Valls G.; Flamary R.; Rakotomamonjy A.
Data(s)

2010

Resumo

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.

Identificador

http://serval.unil.ch/?id=serval:BIB_561A1E176AAD

doi:10.1109/MLSP.2010.5589202

isbn:978-1-4244-7876-7

Idioma(s)

en

Fonte

IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Grenoble, France, pp. 41-46

Tipo

info:eu-repo/semantics/article

article