Learning spatial filters for multispectral image segmentation
Data(s) |
2010
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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 |