Multilinear spectral unmixing of hyperspectral multiangle images

Miguel Angel Veganzones 1 Jérémy E. Cohen 1 R Cabral Farias 1 R Marrero 2 Jocelyn Chanussot 3 Pierre Comon 1
1 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
2 Planeto
IPAG - Institut de Planétologie et d'Astrophysique de Grenoble
3 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : 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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Direction d'ouvrage, Proceedings, Dossier
23rd European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France. pp.749-753, 2015, Proceedings of the 23rd European Signal Processing Conference (EUSIPCO)
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Miguel Angel Veganzones, Jérémy E. Cohen, R Cabral Farias, R Marrero, Jocelyn Chanussot, et al.. Multilinear spectral unmixing of hyperspectral multiangle images. 23rd European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France. pp.749-753, 2015, Proceedings of the 23rd European Signal Processing Conference (EUSIPCO). 〈hal-01158900〉

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