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In hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a unique spectral signature, called an endmember, can be associated with each pure material present in the scene. This assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition,...
In hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually supposed that a single spectral signature, called an endmember, can be associated with each pure material present in the scene. Such an assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the...
When performing blind unmixing of hyperspectral images, the mixing model properties determine which unmixing methods are adequate and how their parameters should be set. One should therefore derive these properties before unmixing, directly from the observed mixed data. We show how to deduce such properties from the eigenvalues of the correlation and covariance matrices of observed hyperspectral images,...
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