NDLI: Unsupervised Hyperspectral Unmixing Based on Constrained Nonnegative Matrix Factorization and Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Jiantao Cui Xiaorun Li
Copyright Year	2010
Abstract	The constrained nonnegative matrix factorization algorithm (CNMF) has previously been shown to be a useful method to solve the unmixing problem in hyper spectral remote sensing images, but it also has some key weaknesses which affect its applied range. It’s sensitive to the initial values, and easily falls to the local minimum. To solve the problems, a new intelligent optimization method#x2014;PSO(Particle Swarm Optimization) algorithm is combined with CNMF. The end members and abundance fractions obtained by CNMF are adopted as the initial values of PSO, the optimal solution of PSO is in reverse as the new initial value in the next running of CNMF, and this procedure is repeated until the global optimal solution is achieved. The experimental results based on synthetic data and real images demonstrate that the proposed method outperforms the standard CNMF algorithm and CNMF with output of VCA as its initial values.
Starting Page	376
Ending Page	380
File Size	571798
Page Count	5
File Format	PDF
ISBN	9781424492473
DOI	10.1109/GCIS.2010.78
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	global minimum local minimum linear spectral mixture model (LSMM) Matrix decomposition Vertex component analysis (VCA) particle swarm optimization (PSO) Particle swarm optimization constrained nonnegative matrix factorization (CNMF) Pixel Optimization Hyperspectral imaging
Content Type	Text
Resource Type	Article

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