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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.
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https://hal.archives-ouvertes.fr/hal-00528923
Contributor : Rémi Flamary <>
Submitted on : Friday, October 22, 2010 - 6:52:45 PM
Last modification on : Friday, November 1, 2019 - 4:46:06 PM
Long-term archiving on: : Sunday, January 23, 2011 - 3:04:46 AM

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  • HAL Id : hal-00528923, version 1

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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⟩

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