Implementation strategies for hyperspectral unmixing using Bayesian source separation. - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2010

Implementation strategies for hyperspectral unmixing using Bayesian source separation.

Résumé

Bayesian positive source separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical nonnegativity of spectra and abundances has to be ensured, such as in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though nonnegativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has so far been limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy that allows one to apply these algorithms on a full hyperspectral image, as it is typical in earth and planetary science, is introduced. The effects of pixel selection and the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different data sets have been used: a synthetic one and a real hyperspectral image from Mars.
Fichier principal
Vignette du fichier
Schmidt_4287.pdf (1.11 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03556769 , version 1 (04-02-2022)

Identifiants

Citer

Frédéric Schmidt, Albrecht Schmidt, Erwan Tréguier, Maël Guiheneuf, Saïd Moussaoui, et al.. Implementation strategies for hyperspectral unmixing using Bayesian source separation.. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (11), pp.4003-4013. ⟨10.1109/TGRS.2010.2062190⟩. ⟨hal-03556769⟩
257 Consultations
37 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More