Unmixing Hyperspectral Data
نویسندگان
چکیده
In hyperspectral imagery one pixel typically consists of a mixture of the re ectance spectra of several materials, where the mixture coe cients correspond to the abundances of the constituting materials. We assume linear combinations of re ectance spectra with some additive normal sensor noise and derive a probabilistic MAP framework for analyzing hyperspectral data. As the material reectance characteristics are not know a priori, we face the problem of unsupervised linear unmixing. The incorporation of di erent prior information (e.g. positivity and normalization of the abundances) naturally leads to a family of interesting algorithms, for example in the noise-free case yielding an algorithm that can be understood as constrained independent component analysis (ICA). Simulations underline the usefulness of our theory.
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