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To be or not to be convex? A study on regularization in hyperspectral image classification


Tuia, Devis; Flamary, Rémi; Barlaud, Michel (2015). To be or not to be convex? A study on regularization in hyperspectral image classification. In: IGARSS 2015, Milan (Italy), 26 July 2015 - 31 July 2015, 4947-4950.

Abstract

Hyperspectral image classification has long been dominated by convex models, which provide accurate decision functions exploiting all the features in the input space. However, the need for high geometrical details, which are often satisfied by using spatial filters, and the need for compact models (i.e. re- lying on models issued form reduced input spaces) has pushed research to study alternatives such as sparsity inducing regu- larization, which promotes models using only a subset of the input features. Although successful in reducing the number of active inputs, these models can be biased and sometimes offer sparsity at the cost of reduced accuracy. In this pa- per, we study the possibility of using non-convex regulariza- tion, which limits the bias induced by the regularization. We present and compare four regularizers, and then apply them to hyperspectral classification with different cost functions.

Abstract

Hyperspectral image classification has long been dominated by convex models, which provide accurate decision functions exploiting all the features in the input space. However, the need for high geometrical details, which are often satisfied by using spatial filters, and the need for compact models (i.e. re- lying on models issued form reduced input spaces) has pushed research to study alternatives such as sparsity inducing regu- larization, which promotes models using only a subset of the input features. Although successful in reducing the number of active inputs, these models can be biased and sometimes offer sparsity at the cost of reduced accuracy. In this pa- per, we study the possibility of using non-convex regulariza- tion, which limits the bias induced by the regularization. We present and compare four regularizers, and then apply them to hyperspectral classification with different cost functions.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:31 July 2015
Deposited On:14 Jan 2016 10:31
Last Modified:08 Dec 2017 16:58
Publisher:IEEE
ISBN:978-1-4799-7929-5
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/IGARSS.2015.7326942
Official URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7326942

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