Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence

Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and...

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Autores principales: E. M. M. B. Ekanayake, H. M. H. K. Weerasooriya, D. Y. L. Ranasinghe, S. Herath, B. Rathnayake, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, H. M. V. R. Herath
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Blind source separation
constrained
endmember independence
Gaussianity
hyperspectral unmixing (HU)
kurtosis
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Acceso en línea:https://doaj.org/article/9da50be38a264431ad6d9883076df7be
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https://doaj.org/article/9da50be38a264431ad6d9883076df7be

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