Nonconvex Regularization in Remote Sensing

Tuia, Devis

doi:10.1109/TGRS.2016.2585201

2016-11-01

Tuia, Devis *

Rémi Flamary

Barlaud, Michel

DOI：10.1109/TGRS.2016.2585201

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摘要

En 中文

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 problemat hand. Finally, we also provide a fully functional toolbox for the community.

Keyword:

Classification

hyperspectral

nonconvex

regularization

remote sensing

sparsity

unmixing

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期刊

IEEE Transactions on Geoscience and Remote Sensing

IF:

8.6

论文数:

2.1W

被引数:

10.7W

机构

centre national de la recherche scientifique (cnrs)

学者数:

24.5W

论文数: 18.2W

被引数: 279

university of zurich

学者数:

5.1W

论文数: 4.0W

被引数: 65

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