[…]’New hyperspectral missions will collect huge amounts of hyperspectral data. Besides, it is possible now to acquire time series and multiangular hyperspectral images. The process and analysis of these big data collections will require common hyperspectral techniques to be adapted or reformulated. The tensor decomposition, a.k.a. multiway analysis, is a technique to decompose multiway arrays, that is, hypermatrices with more than two dimensions (ways). Hyperspectral time series and multiangular acquisitions can be represented as a 3-way tensor. Here, we apply Canonical Polyadic tensor decomposition techniques to the blind analysis of hyperspectral big data.'[…]
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By Miguel Angel Veganzones, Jérémy E. Cohen, Rodrigo Cabral Farias, Jocelyn Chanussot, Pierre Comon
Source: hal.archives-ouvertes.fr/