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Communication Dans Un Congrès Année : 2020

Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures (iTWIST 2020)

Résumé

In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one. The proposed algorithm, referred to as Successive Nonnegative Projection Algorithm for Linear Quadratic mixtures (SNPALQ), extends the Successive Nonnegative Projection Algorithm (SNPA), designed to address the unmixing problem under a linear model. By explicitly modeling the product terms inherent to the LQ model along the iterations of the SNPA scheme, the nonlinear contributions in the mixing are mitigated, thus improving the separation quality. The approach is shown to be relevant in a realistic numerical experiment.
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Dates et versions

hal-03108196 , version 1 (13-01-2021)

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Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon. Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures (iTWIST 2020). International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques (iTWIST 2020), Dec 2020, Nantes (virtual), France. ⟨hal-03108196⟩
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