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A Machine Learning Approach for Material Detection in Hyperspectral Images
Marée, Raphaël; Stevens, Benjamin; Geurts, Pierre et al.
2009In Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09)
Peer reviewed
 

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Keywords :
image; hyperspectral; extra-trees
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Marée, Raphaël  ;  Université de Liège - ULiège > GIGA-Management : Plateforme bioinformatique
Stevens, Benjamin ;  PEPITE SA
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Guern, Yves;  ATIS SA
Mack, Philippe;  PEPITE SA
Language :
English
Title :
A Machine Learning Approach for Material Detection in Hyperspectral Images
Publication date :
2009
Event name :
6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum
Event place :
Miami, United States
Audience :
International
Main work title :
Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09)
Publisher :
IEEE
Peer reviewed :
Peer reviewed
Name of the research project :
HAWKEYE SST4-CT-2005-516168
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
UE - Union Européenne [BE]
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since 16 June 2009

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