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Implementation strategies for hyperspectral unmixing using Bayesian source separation | | Frederic Schmidt
; Albrecht Schmidt
; Erwan Treguier
; Mael Guiheneuf
; Said Moussaoui
; Nicolas Dobigeon
; | | Date: |
4 Jan 2010 | | Abstract: | Bayesian source separation with positivity constraint (BPSS) is a useful
unsupervised approach for hyperspectral data unmixing. The main interest of
this approach is to ensure the positivity of the unmixed component spectra and
abundances. Moreover, a recent extension has been proposed to impose the
sum-to-one (full additivity) constraint to the estimated abundances.
Unfortunately, even if positivity and full additivity are two necessary
properties to get physically interpretable results, the use of BPSS algorithms
is limited by high computation time and large memory requirements since these
Bayesian algorithms employ Markov Chain Monte Carlo methods. This article
introduces an implementation strategy which allows one to apply such algorithms
to a full hyperspectral image, as typical in Earth and Planetary Science, with
reduced computation cost. We study the effect of pixel selection as a
preprocessing step and discuss the impact of such preprocessing on the
relevance of the estimated component spectra and abundance maps as well as on
the whole computation times. For that purpose, we use two different datasets: a
synthetic one and a real hyperspectral image from Mars. | | Source: | arXiv, 1001.0499 | | Services: | Forum | Review | PDF | Favorites | |
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