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Méthodes de démélange non-linéaires pour l'imagerie hyperspectrale

Abstract : In this thesis , we present several aspects of hyperspectral imaging technology , while focusing on the problem of non- linear unmixing . We have proposed three solutions for this task. The first one is integrating the advantages of manifold learning in classical unmixing methods to design their nonlinear versions . Results with data generated on a well-known manifold- the " Swissroll " - seem promising. The methods work much better with the increase in non- linearity compared with their linear version. However, the absence of constraint of non- negativity in these methods remains an open question for improvements . The second proposal is using the pre-image method for estimating an inverse transformation of the data form pixel space to abundance of space . The adoption of spatial information as " total variation " is also introduced to make the algorithm more robust to noise . However, the problem of obtaining ground truth data required for learning step limits the application of such algorithms.
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Submitted on : Friday, February 21, 2014 - 1:07:08 PM
Last modification on : Tuesday, October 19, 2021 - 7:00:51 PM
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  • HAL Id : tel-00950388, version 1



Nguyen Nguyen Hoang. Méthodes de démélange non-linéaires pour l'imagerie hyperspectrale. Autre. Université Nice Sophia Antipolis, 2013. Français. ⟨NNT : 2013NICE4113⟩. ⟨tel-00950388⟩



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