TITLE:
CUR Based Initialization Strategy for Non-Negative Matrix Factorization in Application to Hyperspectral Unmixing
AUTHORS:
Li Sun, Gengxin Zhao, Xinpeng Du
KEYWORDS:
Nonnegative Matrix Factorization, Hyperspectral Image, Hyperspectral Unmixing, Initialization Method
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.4 No.4,
April
13,
2016
ABSTRACT: 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.