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SPECTRAL UNMIXING WITH SPARSITY AND STRUCTURING CONSTRAINTS

Abstract : This paper addresses the linear spectral unmixing problem, by incorporating different constraints that may be of interest in order to cope with spectral variability: sparsity (few nonzero abundances), group exclusivity (at most one nonzero abundance within subgroups of endmembers) and significance (non-zero abundances must exceed a threshold). We show how such problems can be solved exactly with mixed-integer programming techniques. Numerical simulations show that solutions can be computed for problems of limited, yet realistic , complexity, with improved estimation performance over existing methods, but with higher computing time. Index Terms-sparse spectral unmixing, L0-norm optimization , structured sparsity, mixed-integer programming.
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Contributor : Ramzi Ben Mhenni Connect in order to contact the contributor
Submitted on : Wednesday, February 27, 2019 - 5:34:25 PM
Last modification on : Wednesday, April 27, 2022 - 3:50:25 AM

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  • HAL Id : hal-02051443, version 1

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Ramzi Ben Mhenni, Sébastien Bourguignon, Jordan Ninin, Frédéric Schmidt. SPECTRAL UNMIXING WITH SPARSITY AND STRUCTURING CONSTRAINTS. IEEE Whispers (2018) : 9th Workshop on Hyperspectral Image and Signal Processing, Sep 2018, Amsterdam, Netherlands. ⟨hal-02051443⟩

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