Nonconvex Regularization in Remote Sensing

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 imp...

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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 54; no. 11; pp. 6470 - 6480
Main Authors Tuia, Devis, Flamary, Remi, Barlaud, Michel
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Classification
Complexity theory
Data models
Estimation
hyperspectral
Hyperspectral sensors
nonconvex
Optimization
regularization
remote sensing
sparsity
Training
unmixing
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Summary: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 (ℓ1) 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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2585201