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Auteur Pierre-Antoine Thouvenin |
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Modeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)
Titre : Modeling spatial and temporal variabilities in hyperspectral image unmixing Type de document : Thèse/HDR Auteurs : Pierre-Antoine Thouvenin, Auteur ; Nicolas Dobigeon, Directeur de thèse ; Jean-Yves Tourneret, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2017 Importance : 191 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] amplitude
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données multitemporelles
[Termes IGN] image hyperspectrale
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processus stochastique
[Termes IGN] séparation aveugle de source
[Termes IGN] signature spectrale
[Termes IGN] variabilitéIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data – referred to as endmembers – and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape
and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.Note de contenu : Introduction
1- Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
2- A Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images
3- Online unmixing of multitemporal hyperspectral images
4- A partially asynchronous distributed unmixing algorithm
Conclusions et perspectivesNuméro de notice : 25812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2017 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017INPT0068 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95075