Spectral unmixing based on nonnegative matrix factorization with local smoothness constraint
Data(s) |
01/01/2015
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Resumo |
Spectral unmixing (SU) is an emerging problem in the remote sensing image processing. Since both the endmember signatures and their abundances have nonnegative values, it is a natural choice to employ the attractive nonnegative matrix factorization (NMF) methods to solve this problem. Motivated by that the abundances are sparse, the NMF with local smoothness constraint (NMF-LSC) is proposed in this paper. In the proposed method, the smoothness constraint is utilized to impose the sparseness, instead of the traditional L1-norm which is restricted by the underlying column-sum-to-one requirement of the to the abundance matrix. Simulations show the advantages of our algorithm over the compared methods. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30081735/yang-spectralunmixing-2015.pdf http://dro.deakin.edu.au/eserv/DU:30081735/yang-spectralunmixing-evid-2015.pdf http://www.dx.doi.org/10.1109/ChinaSIP.2015.7230481 |
Direitos |
2015, IEEE |
Palavras-Chave | #spectral unmixing #nonnegative matrix factorization #smoothness constraint |
Tipo |
Conference Paper |