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Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures...
Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. When hyperspectral unmixing relies on the use of spectral libraries (dictionaries of pure spectra), the sparse regression problem to be solved is severely ill-conditioned and time-consuming. This is due, on the one hand, to the presence of very similar signatures in the library and, on the other, to the...
Methods for fast and accurate identification and quantification of the composition of pharmaceutical mixtures are important in many scientific and industrial applications. When this goal is approached via hyperspectral data analysis, the problem becomes one of hyperspectral unmixing, where the goal is to identify the pure materials (also called endmembers) present in a mixture, as well as their relative...
Hyperspectral unmixing has recently been addressed as a sparse regression problem by using predefined spectral libraries instead of image-derived endmembers in the unmixing process. This new approach has attracted much attention, as it sidesteps well known obstacles met in endmember extraction, such as the stopping criteria for the extraction process (represented by the number of endmembers needed...
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