DocumentCode :
3715934
Title :
Multilinear spectral unmixing of hyperspectral multiangle images
Author :
M. A. Veganzones;J. Cohen;R. Cabrai Farias;R. Marrero;J. Chanussot;P. Comon
Author_Institution :
GIPSA-Lab, St Martin d´Heres cedex, France
fYear :
2015
Firstpage :
744
Lastpage :
748
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.
Keywords :
"Tensile stress","Hyperspectral imaging","Europe","Matrix decomposition","Image coding","Least squares approximations"
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2015 23rd European
Electronic_ISBN :
2076-1465
Type :
conf
DOI :
10.1109/EUSIPCO.2015.7362482
Filename :
7362482
Link To Document :
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