Computer Science ›› 2018, Vol. 45 ›› Issue (12): 251-254,278.doi: 10.11896/j.issn.1002-137X.2018.12.041

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Hyperspectral Unmixing Algorithm Based on Dual Graph-regularized Semi-supervised NMF

ZOU Li1, CAI Xi-biao1, SUN Jing2, SUN Fu-ming1   

  1. (School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou,Liaoning 121001,China)1
    (School of Software Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China)2
  • Received:2017-12-20 Online:2018-12-15 Published:2019-02-25

Abstract: In hyperspectral images,the existence of mixed pixels greatly impedes the development of hyperspectral remote sensing technology.Therefore,how to carry out unmixing accurately and efficiently in the process of using spectral images is a key problem.For hyperspectral unmixing,using original non-negative matrix factorization (NMF) algorithm faces some difficulties,for example,the objective function is non-convex function,so it is difficult to solve the global optimal solution.Besides,the pure pixel like element doesn’t exist in mixed pixel.In order to solve these problems,this paper proposed a mixed pixel unmixing algorithm namely dual graph-regularized constrained semi-supervised NMF (DCNMF) .This algorithm adopts gradient descent algorithm and iterative updating rule,considers the geometric structures of hyperspectral data manifold and the spectral feature manifold,and can jump out of the local extremum,thus solving the global optimal solution.Real hyperspectral image data simulation experiments show that DCNMF algorithm can be used to decompose the mixed pixel accurately and efficiently,enhancing the effect of unmixing,improving the accuracy of mixing,saving the computing time and speeding up convergence.

Key words: Hyperspectral images, Mixed pixel disintegration, Nonnegative matrix factorization, Bigraph regularization

CLC Number: 

  • TP391
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