Title :
High-Efficiency Hyperspectral Unmixing Based on Band Selection
Author :
Yang Zhou ; Xiaorun Li ; Jiantao Cui
Author_Institution :
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
Abstract :
Hyper spectral unmixing (HU) is important for ground objects identification. Due to the mass data hyper spectral sensors bring, band selection plays an important role in boosting efficiency of HU. This paper proposes a high-efficiency approach of HU that carries out two modified algorithms of band selection followed by nonnegative matrix factorization (NMF), which are linear prediction (LP) combined with K-L divergence and mutual information (MI). Experiment results based on simulated data and real hyper spectral imagery demonstrate that the proposed scheme is more efficient than initial NMF in HU.
Keywords :
image processing; matrix decomposition; K-L divergence; NMF; boosting efficiency; ground objects identification; high-efficiency approach; hyperspectral imagery; hyperspectral unmixing band selection; linear prediction; mass data hyper spectral sensor; mutual information; nonnegative matrix factorization; Algorithm design and analysis; Hyperspectral imaging; Mathematical model; Mutual information; Runtime; band selection; hyperspectral unmixing; nonnegative matrix factorization;
Conference_Titel :
Intelligent Systems (GCIS), 2012 Third Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4673-3072-5
DOI :
10.1109/GCIS.2012.39
