Primal-dual interior point optimization for penalized least squares estimation of abundance maps in hyperspectral imaging

Abstract : The estimation of abundance maps in hyperspectral imaging requires the resolution of an optimization problem under nonnegativity and sum to one constraints. Assuming that the spectral signatures of the image components have been previously determined by an endmember extraction algorithm, we propose here a primal-dual interior point algorithm for the estimation of their fractional abundances. In comparison with the reference method FCLS, our algorithm has the advantage of a reduced computational cost, especially in the context of large scale images and allows to deal with a penalized criterion favoring the spatial smoothness of abundance maps. The performances of the proposed approach are discussed with the help of synthetic and real examples.
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https://hal.archives-ouvertes.fr/hal-00697880
Contributor : Saïd Moussaoui <>
Submitted on : Wednesday, May 16, 2012 - 1:12:48 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:04 PM

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  • HAL Id : hal-00697880, version 1

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Saïd Moussaoui, Emilie Chouzenoux, Jérôme Idier. Primal-dual interior point optimization for penalized least squares estimation of abundance maps in hyperspectral imaging. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Jun 2012, Shanghai, China. pp.CD ROM. ⟨hal-00697880⟩

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