Constrained Kaczmarz's Cyclic Projections for Unmixing Hyperspectral Data - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Constrained Kaczmarz's Cyclic Projections for Unmixing Hyperspectral Data

Résumé

The estimation of fractional abundances under physical constraints is a fundamental problem in hyperspectral data processing. In this paper, we propose to adapt Kaczmarz's cyclic projections to solve this problem. The main contribution of this work is two-fold: On the one hand, we show that the non-negativity and the sum-to-one constraints can be easily imposed in Kaczmarz's cyclic projections, and on the second hand, we illustrate that these constraints are advantageous in the convergence behavior of the algorithm. To this end, we derive theoretical results on the convergence performance, both in the noiseless case and in the case of noisy data. Experimental results show the relevance of the proposed method.
Fichier principal
Vignette du fichier
13.eusipco.Kaczmarz.pdf (258.64 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01966006 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01966006 , version 1

Citer

Paul Honeine, Henri Lantéri, Cédric Richard. Constrained Kaczmarz's Cyclic Projections for Unmixing Hyperspectral Data. Proc. 21th European Conference on Signal Processing (EUSIPCO), 2013, Marrakech, Morocco. pp.1-5. ⟨hal-01966006⟩
57 Consultations
29 Téléchargements

Partager

Gmail Facebook X LinkedIn More