Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance

Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse non...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Jingyan Zhang, Xiangrong Zhang, Licheng Jiao
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2021-06-01
Sarja:	Remote Sensing
Aiheet:	hyperspectral image spectral unmixing endmember independence spatial weight manifold learning
Linkit:	https://www.mdpi.com/2072-4292/13/12/2348
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