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Contributions to Hyperspectral Unmixing

Abstract : Spectral Unmixing has been an active area of research during the last years and found its application in domains including but not limited to remote sensing, audio signal processing and chemistry. Despite their very high spectral resolution, hyperspectral images (HSI) are known to be of low spatial resolution. This low resolution is a relative notion and is due to technological limitations of the HSI captors. As a consequence the values of HSI pixels are likely to be mixtures Of diferent materials in the scene. hyperspectral Unmixing then can be dened as an inverse procedure that consists in identifying in each pixel the amount of pure elements contributing to the pixels mixture. The total number of pure elements (also called endmembers) and the number of them included in one pixel are two informations tricky to retrieve. The simplest situation is when both the total number and type of endmembers within the scene are known and associated with a linear mixing process assumption. Though efficient in some situations, this linearity assumption does not generally hold in real world scenarios. Also in most cases the knowledge regarding the endmember signature of a specic material is not exact, raising the need to account for variations among different representations of the same material. Last but not least existence of anomalies and noise is a ubiquitous issue affecting the accuracy of the estimations. In this thesis, the three aforementioned issues were mainly brought into light and by introducing two original algorithms, defined within different mathematical frameworks, solutions to these open problems has provided. The first contribution using the applications of kernel theory proposes a new unsupervised algorithm (SAGA+) for representation of the non-linear manifold embedding the data while through a simultaneous anomaly detection procedure makes sure that the representation of the manifold hall is not being distorted at the presence of anomalies. The second major contribution of this PhD focuses mainly on the issue of endmember variability and by exploiting the notion of overcomplete dictionary tries to address this problem. This supervised algorithm (OT-unmixing) which is based on the optimal transport theory is comparable to the second step of SAGA+, as it solves an inversion problem and calculates the sparse representation of the original pixels through generation of the abundance maps. A case study in the context of CATUT project for land surface temperature estimation is described in the last part of this work where the two algorithms used for unmixing of airborne hyperspectral remote sensing.
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Sina Nakhostin. Contributions to Hyperspectral Unmixing. Signal and Image processing. Université de Bretagne Sud, 2017. English. ⟨NNT : 2017LORIS477⟩. ⟨tel-01867812⟩

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