Cross-validation of blindly separated interstellar dust spectra

International audienceIn this paper, we investigate the validation of Blind Source Separation (BSS) methods applied to interstellar dust hyperspectral data cubes. Since the original source signals are unknown, we cannot measure the separation accuracy by means of classical objective criteria. As a consequence, we here propose a cross-validation of the extracted spectra by applying to the measured data various BSS techniques based on different criteria. We show that, with all these methods, we obtain quite the same (physically relevant) estimated interstellar dust spectra. Moreover, we then derive a spatial structure of the emission of the chemical species, which is not used in the separation step and which is physically relevant

Source separation algorithms for the analysis of hyperspectral observations of very small interstellar dust particles

International audienceThe mid-infrared (mid-IR) spectrum of our galaxy is dominated by continuum and band emission due to carbonaceous very small dust particles amongst which are polycyclic aromatic hydrocarbon (PAH) molecules. Because they absorb the UV photons ofmassive stars and re-emit this energy in the infrared, IR spectro-imaging of extended interstellar (or circumstellar) regions is a powerful tool to diagnose the nature of these particles together with the local physical conditions. In this paper, we review how the applications of blind / bayesian source separation (BSS) methods applied to mid-IR hyperspectral data can help analyzing these data. We then discuss, in the light of simulations in progress, how BSS methods could be used to identify specific PAH molecules in the interstellar medium when applied to the hyper-spectral data of forthcoming IR telescopes (Herschel, SOFIA, SPICA)

Séparation aveugle de sources Markoviennes et non-stationnaires

Dans cet article, nous proposons une extension, au cas non-stationnaire, d'une méthode de séparation aveugle de sources stationnaires et autocorrélées que nous avons développée précédemment. La méthode présentée se base sur une approche de maximum de vraisemblance et permet de simplifier les fonctions de densité de probabilité conditionnelles (pdfs) des sources en les modélisant par des processus de Markov. Pour tenir compte de la non-stationnarité des sources, deux méthodes, basées respectivement sur un découpage par blocs et une approche à noyau, sont utilisées pour estimer les fonctions score des sources. Les tests de séparation réalisés sur des signaux temporels et des images prouvent les très bonnes performances de nos méthodes

Méthodes markoviennes pour la séparation aveugle de signaux et images

TOULOUSE3-BU Sciences (315552104) / SudocTOULOUSE-Observ. Midi Pyréné (315552299) / SudocSudocFranceF

A map-based NMF approach to hyperspectral image unmixing using a linear-quadratic mixture model

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A new unsupervised method for hyperspectral image unmixing using a linear-quadratic model

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A second-order blind source separation method for bilinear mixtures

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