Analyzing Hyperspectral Data with Independent Component Analysis
نویسندگان
چکیده
Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about diierent materials within a pixel to be obtained. The problem of spectrally unmixing materials may be viewed as a speciic case of the blind source separation problem where data consists of mixed signals (in this case minerals) and the goal is to determine the contribution of each mineral to the mix without prior knowledge of the minerals in the mix. The technique of Independent Component Analysis (ICA) assumes that the spectral components are close to statistically independent and provides an unsupervised method for blind source separation. We introduce contextual ICA in the context of hyperspectral data analysis and apply the method to mineral data from synthetically mixed minerals and real image signatures.
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