Skip to Main content Skip to Navigation
New interface
Conference papers

Learning spatial filters for multispectral image segmentation.

Abstract : We present a novel filtering method for multispectral satel- lite 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 car- ried out on multiclass one-against-all classification and tar- get detection show the capabilities of the learned spatial fil- ters.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Rémi Flamary Connect in order to contact the contributor
Submitted on : Friday, October 22, 2010 - 6:52:45 PM
Last modification on : Saturday, November 19, 2022 - 1:24:03 PM
Long-term archiving on: : Sunday, January 23, 2011 - 3:04:46 AM


Files produced by the author(s)


  • HAL Id : hal-00528923, version 1


Devis Tuia, Rémi Flamary, Gustavo Camps-Valls, Alain Rakotomamonjy. Learning spatial filters for multispectral image segmentation.. Machine Learning for Signal Processing, Aug 2010, Kittila, Finland. pp.1-6. ⟨hal-00528923⟩



Record views


Files downloads