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Méthodes rapides de traitement d’images hyperspectrales. Application à la caractérisation en temps réel du matériau bois

Abstract : This PhD dissertation addresses the problem of on-line unmixing of hyperspectral images acquired by a pushbroom imaging system, for real-time characterization of wood. The first part of this work proposes an on-line mixing model based on non-negative matrix factorization. Based on this model, three algorithms for on-line sequential unmixing, using multiplicative update rules, the Nesterov optimal gradient and the ADMM optimization (Alternating Direction Method of Multipliers), respectively, are developed. These algorithms are specially designed to perform the unmixing in real time, at the pushbroom imager acquisition rate. In order to regularize the estimation problem (generally ill-posed), two types of constraints on the endmembers are used: a minimum dispersion constraint and a minimum volume constraint. A method for the unsupervised estimation of the regularization parameter is also proposed, by reformulating the on-line hyperspectral unmixing problem as a bi-objective optimization. In the second part of this manuscript, we propose an approach for handling the variation in the number of sources, i.e. the rank of the decomposition, during the processing. Thus, the previously developed on-line algorithms are modified, by introducing a hyperspectral library learning stage as well as sparse constraints allowing to select only the active sources. Finally, the third part of this work consists in the application of these approaches to the detection and the classification of the singularities of wood.
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Submitted on : Thursday, February 13, 2020 - 5:06:58 PM
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Ludivine Nus. Méthodes rapides de traitement d’images hyperspectrales. Application à la caractérisation en temps réel du matériau bois. Traitement du signal et de l'image [eess.SP]. Université de Lorraine, 2019. Français. ⟨NNT : 2019LORR0163⟩. ⟨tel-02478101⟩



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