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Optics / Dimension reduction / Cartography / Spectroscopy / Infrared imaging / Hyperspectral imaging / Feature selection / Random forest / Remote sensing / Statistics / Machine learning / Imaging


CONTRIBUTION OF BAND SELECTION AND FUSION FOR HYPERSPECTRAL CLASSIFICATION Nesrine Chehata (a,b), Arnaud Le Bris (c) and Safa Najjar (d) (a) IRD/UMR LISAH El Menzah 4, Tunis, Tunisia (b) EA 4592 G&E, ENSEGID-IPB, Univers
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Document Date: 2014-08-08 05:12:32

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City

Pessac Cedex / Clustering / Paris / Bhattacharyya / Tunis / /

Company

Academic Press Professional Inc. / Pearson / ROSIS / Knowledgebased Systems / /

Country

Tunisia / France / /

Event

M&A / /

Facility

University of Bordeaux / /

IndustryTerm

inductive learning algorithms / superspectral camera systems / satellite hyperspectral imagery / genetic algorithm / /

MusicGroup

SELECTION / AVIRIS / Ranking / Random / /

NaturalFeature

Random Forests / Random forest / /

Organization

ENSI Ecole Nationale / Indian Pines / Salinas and Pavia Centre / University of Bordeaux / /

Person

Safa Najjar / Isabelle Guyon / Arnaud Le Bris / Bordeaux / /

PublishedMedium

Journal of Machine Learning Research / Machine Learning / /

Technology

Bioinformatics / remote sensing / Machine Learning / inductive learning algorithms / DNA Chip / /

SocialTag