JAMP  Vol.4 No.4 , April 2016
CUR Based Initialization Strategy for Non-Negative Matrix Factorization in Application to Hyperspectral Unmixing

Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well.

Cite this paper: Sun, L. , Zhao, G. , Du, X. (2016) CUR Based Initialization Strategy for Non-Negative Matrix Factorization in Application to Hyperspectral Unmixing. Journal of Applied Mathematics and Physics, 4, 614-617. doi: 10.4236/jamp.2016.44068.

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