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Communication Dans Un Congrès Année : 2015

Multilinear spectral unmixing of hyperspectral multiangle images

Résumé

Spectral unmixing is one of the most important and studied topics in hyperspectral image analysis. By means of spectral unmixing it is possible to decompose a hyperspectral image in its spectral components, the so-called endmembers, and their respective fractional spatial distributions, so-called abundance maps. New hyperspectral missions will allow to acquire hyperspectral images in new ways, for instance, in temporal series or in multi-angular acquisitions. Working with these incoming huge databases of multi-way hyperspec-tral images will raise new challenges to the hyperspectral community. Here, we propose the use of compression-based non-negative tensor canonical polyadic (CP) decompositions to analyze this kind of datasets. Furthermore, we show that the non-negative CP decomposition could be understood as a multi-linear spectral unmixing technique. We evaluate the proposed approach by means of Mars synthetic datasets built upon multi-angular in-lab hyperspectral acquisitions.
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Dates et versions

hal-01158900 , version 1 (02-06-2015)

Identifiants

  • HAL Id : hal-01158900 , version 1

Citer

Miguel Angel Veganzones, Jérémy E Cohen, Rodrigo Cabral Farias, Ruben Marrero, Jocelyn Chanussot, et al.. Multilinear spectral unmixing of hyperspectral multiangle images. EUSIPCO 2015 - 23th European Signal Processing Conference, IEEE Signal Processing Society, Aug 2015, Nice, France. pp.749-753. ⟨hal-01158900⟩
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