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Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures

Abstract : 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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Contributor : Nicolas Dobigeon <>
Submitted on : Wednesday, January 13, 2021 - 8:56:45 AM
Last modification on : Wednesday, March 31, 2021 - 11:12:23 AM
Long-term archiving on: : Wednesday, April 14, 2021 - 6:13:16 PM


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  • HAL Id : hal-03108196, version 1
  • ARXIV : 2012.04612


Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon. Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures. international Traveling Workshop Interactions between Sparse models and Technology (iTWIST 2020), Sébastien Bourguignon (Centrale Nantes, LS2N, Nantes, France); Cédric Herzet (INRIA Rennes, France); Jérôme Idier (CNRS, LS2N, Nantes, France); Charles Soussen (CentraleSupélec, L2S, Gif-Sur-Yvette, France), Dec 2020, Nantes (virtual), France. ⟨hal-03108196⟩



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