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ICIP
2010
IEEE

Hyper-DEMIX: Blind source separation of hyperspectral images using local ML estimates

10 years 11 months ago
Hyper-DEMIX: Blind source separation of hyperspectral images using local ML estimates
We propose a new method to unmix hyperspectral images. Our method exploits the structure of the material abundance maps by assuming that in some regions of the spatial dimension, only one material is present. Such regions provide a local estimate of the endmember spectrum of the corresponding material. Our main contribution is a new clustering algorithm called Hyper-DEMIX to estimate the endmember spectrum of each material based on such local estimates. The abundance map of each material is then recovered with a binary masking technique. Experimental results over noisy hyperspectral images show the effectiveness of the proposed approach.
Simon Arberet
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICIP
Authors Simon Arberet
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