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Conference papers
Multilinear spectral unmixing of hyperspectral multiangle images
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.
Cited literature [13 references]
https://hal.archives-ouvertes.fr/hal-01158900
Contributor : Miguel Angel Veganzones <>
Submitted on : Tuesday, June 2, 2015 - 10:05:04 AM
Last modification on : Thursday, November 19, 2020 - 3:54:31 PM
Long-term archiving on: : Monday, April 24, 2017 - 9:32:20 PM
Contributor : Miguel Angel Veganzones <>
Submitted on : Tuesday, June 2, 2015 - 10:05:04 AM
Last modification on : Thursday, November 19, 2020 - 3:54:31 PM
Long-term archiving on: : Monday, April 24, 2017 - 9:32:20 PM
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EUSIPCO2015_Veganzones.pdf
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