Spectral Variability in Hyperspectral Unmixing: Multiscale, Tensor, and Neural Network-based Approaches (2021)
Abstract / truncated to 115 words
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EMs), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of the estimated abundances. Therefore, significant effort have been recently dedicated to mitigate the effects of spectral variability in SU. However, many challenges still remain in how to best explore a priori information about the problem in order to improve the quality, the robustness and the efficiency of SU algorithms that account for spectral variability. In this thesis, ... toggle 6 keywordshyperspectral images – endmember variability – spectral unmixing – multiscale – tensors – neural networks
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