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Nonconvex Regularization in Remote Sensing

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Tuia Devis, Flamary Rémi, Barlaud Michel,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal IEEE Transactions on Geoscience and Remote Sensing
Volume (Issue) 54(11)
Page(s) 6470 - 6480
Title of proceedings IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2016.2585201

Open Access

URL https://arxiv.org/abs/1606.07289
Type of Open Access Repository (Green Open Access)

Abstract

© 2016 IEEE. 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 parameterization. We consider regularization via traditional squared (ℓ 2 ) and sparsity-promoting (ℓ 1 ) norms, as well as more unconventional nonconvex regularizers (ℓ 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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