Implementation strategies for hyperspectral unmixing using Bayesian source separation

Details Implementation strategies for hyperspectral unmixing using Bayesian source separation Implementation strategies for hyperspectral unmixing using Bayesian source separation

2010-01-04

arxiv.org/abs/1001.0499v2

IEEE Transactions on Geoscience and Remote Sensing, Nov. 2010, Vol 48, Issue 11, 4003-4013

Astronomy and Astrophysics

Astrophysics

Earth and Planetary Astrophysics

10 pages, 6 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing in the special issue on Hyperspectral Ima

Scientific paper

10.1109/TGRS.2010.2062190

Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has been so far limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy which allows one to apply these algorithms on a full hyperspectral image, as typical in Earth and Planetary Science, is introduced. Effects of pixel selection, the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different dataset have been used: a synthetic one and a real hyperspectral image from Mars.

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