Hyper-spectral Image Analysis with Partially-Latent Regression

Antoine Deleforge 1 Florence Forbes 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The analysis of hyper-spectral images is often needed to recover physical properties of planets. To address this inverse problem, the use of learning methods have been considered with the advantage that, once a relationship between physical parameters and spectra has been established through training, the learnt relationship can be used to estimate parameters from new images underpinned by the same physical model. Within this framework, we propose a partially-latent regression method which maps high-dimensional inputs (spectral images) onto low-dimensional responses (physical parameters). We introduce a novel regression method that combines a Gaussian mixture of locally-linear mappings with a partially-latent variable model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. The method is illustrated on images collected from the Mars planet.
Type de document :
Communication dans un congrès
European Signal Processing Conference, Sep 2014, Lisbon, Portugal. IEEE, pp.1572 - 1576, 2014, <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952574>
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-01019360
Contributeur : Team Perception <>
Soumis le : lundi 7 juillet 2014 - 11:01:57
Dernière modification le : samedi 18 février 2017 - 01:07:06
Document(s) archivé(s) le : mardi 7 octobre 2014 - 11:58:11

Fichiers

Deleforge-hyperspectral.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01019360, version 1

Citation

Antoine Deleforge, Florence Forbes, Radu Horaud. Hyper-spectral Image Analysis with Partially-Latent Regression. European Signal Processing Conference, Sep 2014, Lisbon, Portugal. IEEE, pp.1572 - 1576, 2014, <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952574>. <hal-01019360>

Partager

Métriques

Consultations de
la notice

461

Téléchargements du document

200