Méthodes de séparation aveugle de sources et application à la télédétection spatiale

Cette thèse concerne la séparation aveugle de sources, qui consiste à estimer un ensemble de signaux sources inconnus à partir d'un ensemble de signaux observés qui sont des mélanges à paramètres inconnus de ces signaux sources. C'est dans ce cadre que le travail de recherche de cette thèse concerne le développement et l'utilisation de méthodes linéaires innovantes de séparation de sources pour des applications en imagerie de télédétection spatiale. Des méthodes de séparation de sources sont utilisées pour prétraiter une image multispectrale en vue d'une classification supervisée de ses pixels. Deux nouvelles méthodes hybrides non-supervisées, baptisées 2D-Corr-NLS et 2D-Corr-NMF, sont proposées pour l'extraction de cartes d'abondances à partir d'une image multispectrale contenant des pixels purs. Ces deux méthodes combinent l'analyse en composantes parcimonieuses, le clustering et les méthodes basées sur les contraintes de non-négativité. Une nouvelle méthode non-supervisée, baptisée 2D-VM, est proposée pour l'extraction de spectres à partir d'une image hyperspectrale contenant des pixels purs. Cette méthode est basée sur l'analyse en composantes parcimonieuses. Enfin, une nouvelle méthode est proposée pour l'extraction de spectres à partir d'une image hyperspectrale ne contenant pas de pixels purs, combinée avec une image multispectrale, de très haute résolution spatiale, contenant des pixels purs. Cette méthode est fondée sur la factorisation en matrices non-négatives couplée avec les moindres carrés non-négatifs. Comparées à des méthodes de la littérature, d'excellents résultats sont obtenus par les approches méthodologiques proposées.This thesis concerns the blind source separation problem, which consists in estimating a set of unknown source signals from a set of observed signals which are mixtures of these source signals, with unknown mixing coefficients. In this thesis, we develop and use innovative linear source separation methods for applications in remote sensing imagery. Source separation methods are used and applied in order to preprocess a multispectral image for a supervised classification of this image. Two new unsupervised methods, called 2D-Corr-NLS and 2D-Corr-NMF, are proposed in order to extract abundance maps from a multispectral image with pure pixels. These methods are based on sparse component analysis, clustering and non-negativity constraints. A new unsupervised method, called 2D-VM, is proposed in order to extract endmember spectra from a hyperspectral image with pure pixels. This method is based on sparse component analysis. Also, a new method is proposed for extracting endmember spectra from a hyperspectral image without pure pixels, combined with a very high spatial resolution multispectral image with pure pixels. This method is based on non-negative matrix factorization coupled with non-negative least squares. Compared to literature methods, excellent results are obtained by the proposed methodological approaches

Unsupervised methods in multilingual and multimodal semantic modeling

In the first part of this project, independent component analysis has been applied to extract word clusters from two Farsi corpora. Both word-document and word-context matrices have been considered to extract such clusters. The application of ICA on the word-document matrices extracted from these two corpora led to the detection of syntagmatic word clusters, while the utilization of word-context matrix resulted in the extraction of both syntagmatic and paradigmatic word clusters. Furthermore, we have discussed some potential benefits of this automatically extracted thesaurus. In such a thesaurus, a word is defined by some other words without being connected to the outer physical objects. In order to fill such a gap, symbol grounding has been proposed by philosophers as a mechanism which might connect words to their physical referents. From their point of view, if words are properly connected to their referents, their meaning might be realized. Once this objective is achieved, a new promising horizon would open in the realm of artificial intelligence. In the second part of the project, we have offered a simple but novel method for grounding words based on the features coming from the visual modality. Firstly, indexical grounding is implemented. In this naïve symbol grounding method, a word is characterized using video indexes as its context. Secondly, such indexical word vectors have been normalized according to the features calculated for motion videos. This multimodal fusion has been referred to as the pattern grounding. In addition, the indexical word vectors have been normalized using some randomly generated data instead of the original motion features. This third case was called randomized grounding. These three cases of symbol grounding have been compared in terms of the performance of translation. Besides that, word clusters have been excerpted by comparing the vector distances and from the dendrograms generated using an agglomerative hierarchical clustering method. We have observed that pattern grounding exceled the indexical grounding in the translation of the motion annotated words, while randomized grounding has deteriorated the translation significantly. Moreover, pattern grounding culminated in the formation of clusters in which a word fit semantically to the other members, while using the indexical grounding, some of the closely related words dispersed into arbitrary clusters