Non-convex regularization in remote sensing

Abstract : In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). We compare their properties and advantages on several classification and linear unmixing tasks and provide advices on the choice of the best regularizer for the problem at hand. Finally, we also provide a fully functional toolbox for the community.
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Article dans une revue
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016
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https://hal.archives-ouvertes.fr/hal-01335890
Contributeur : Rémi Flamary <>
Soumis le : mercredi 22 juin 2016 - 14:36:45
Dernière modification le : vendredi 24 juin 2016 - 01:08:32

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Devis Tuia, Rémi Flamary, Michel Barlaud. Non-convex regularization in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016. <hal-01335890>

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