Fast constrained least squares spectral unmixing using primal-dual interior point optimization

Emilie Chouzenoux 1 Maxime Legendre 2 Saïd Moussaoui 3 Jérôme Idier 3
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 ADTSI
IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
3 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
Abstract : Hyperspectral data unmixing aims at identifying the components (endmembers) of an observed surface and at determining their fractional abundances inside each pixel area. Assuming that the spectral signatures of the surface components have been previously determined by an endmember extraction algorithm, or to be part of an available spectral library, the main problem is reduced to the estimation of the fractional abundances. For large hyperspectral image data sets, the estimation of the abundance maps requires the resolution of a large-scale optimization problem subject to linear constraints such as non-negativity and sum less or equal to one. This paper proposes a primal-dual interior-point optimization algorithm allowing a constrained least squares estimation approach. In comparison with existing methods, the proposed algorithm is more flexible since it can handle any linear equality and/or inequality constraint and has the advantage of a reduced computational cost. It also presents an algorithmic structure suitable for a parallel implementation on modern intensive computing devices such as Graphics Processing Units (GPU). The implementation issues are discussed and the applicability of the proposed approach is illustrated with the help of examples on synthetic and real hyperspectral data.
Keywords : spectral unmixing constrained least squares interior-point optimization primal-dual algorithm GPU computing
Type de document :
Article dans une revue
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2014, 7 (1), pp.59-69. 〈10.1109/JSTARS.2013.2266732〉
Liste complète des métadonnées

Littérature citée [49 références]  Voir  Masquer  Télécharger
https://hal.archives-ouvertes.fr/hal-00828013
Contributeur : Emilie Chouzenoux <>
Soumis le : dimanche 14 juillet 2013 - 18:23:56
Dernière modification le : jeudi 5 juillet 2018 - 14:46:13
Document(s) archivé(s) le : mardi 15 octobre 2013 - 02:35:11

Fichier

manuscript.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Collections

LIGM_SIGNAL | LIGM | UNIV-MLV | UPEC-UPEM | UNAM | UNIV-NANTES | IRCCYN-ADTSI | IRCCYN | PARISTECH | ENPC | ESIEE-PARIS

Citation

Emilie Chouzenoux, Maxime Legendre, Saïd Moussaoui, Jérôme Idier. Fast constrained least squares spectral unmixing using primal-dual interior point optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2014, 7 (1), pp.59-69. 〈10.1109/JSTARS.2013.2266732〉. 〈hal-00828013〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

573

Téléchargements de fichiers

975

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD