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Communication Dans Un Congrès Année : 2012

Nonlinear unmixing of hyperspectral images based on multi-kernel learning

Résumé

Nonlinear unmixing of hyperspectral images has generated considerable interest among researchers, as it may overcome some inherent limitations of the linear mixing model. In this paper, we formulate the problem of estimating abundances of a nonlinear mixture of hyperspectral data based on a new multi-kernel learning paradigm. Experiments are conducted using both synthetic and real images in order to illustrate the effectiveness of the proposed method.
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Dates et versions

hal-01966012 , version 1 (27-12-2018)

Identifiants

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Jie Chen, Cédric Richard, Paul Honeine. Nonlinear unmixing of hyperspectral images based on multi-kernel learning. Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2012, Shanghai, China. pp.1-4, ⟨10.1109/WHISPERS.2012.6874231⟩. ⟨hal-01966012⟩
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