Reference : A Machine Learning Approach for Material Detection in Hyperspectral Images
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/14710
A Machine Learning Approach for Material Detection in Hyperspectral Images
English
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
Stevens, Benjamin mailto [PEPITE SA > > > >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Guern, Yves [ATIS SA > > > >]
Mack, Philippe [PEPITE SA > > > >]
2009
Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09)
IEEE
Yes
No
International
6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum
Miami
USA
[en] image ; hyperspectral ; extra-trees
[en] In this paper we propose a machine learning approach
for the detection of gaseous traces in thermal infra red
hyperspectral images. It exploits both spectral and spatial
information by extracting subcubes and by using extremely
randomized trees with multiple outputs as a classifier.
Promising results are shown on a dataset of more
than 60 hypercubes.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Union Européenne = European Union - UE = EU
HAWKEYE SST4-CT-2005-516168
http://hdl.handle.net/2268/14710
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/MSGGM09

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
maree-subcubes-otcbvs-cvpr09.pdfAuthor postprint241.24 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.