Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale

Abstract : In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches.
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Fatima Zohra Benhalouche. Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale. Traitement des images. Université Paul Sabatier - Toulouse III, 2018. Français. ⟨NNT : 2018TOU30083⟩. ⟨tel-02100067⟩

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