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A projected gradient-based algorithm to unmix hyperspectral data

Zandifar, A ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. Publisher: 2012
  3. Abstract:
  4. This paper presents a method to solve hyperspectral unmixing problem based on the well-known linear mixing model. Hyperspectral unmixing is to decompose observed spectrum of a mixed pixel into its constituent spectra and a set of corresponding abundances. We use Nonnegative Matrix Factorization (NMF) to solve the problem in a single step. The proposed method is based on a projected gradient NMF algorithm. Moreover, we modify the NMF algorithm by adding a penalty term to include also the statistical independence of abundances. At the end, the performance of the method is compared to two other algorithms using both real and synthetic data. In these experiments, the algorithm shows interesting performance in spectral unmixing and surpasses the other methods
  5. Keywords:
  6. hyper-spectral imagery ; linear mixture model (LMM) ; non-negative matrix factorization (NMF) ; Gradient based algorithm ; HyperSpectral ; Hyperspectral Data ; Hyperspectral unmixing ; Linear mixing models ; Linear mixture models ; Mixed pixel ; Nonnegative matrix factorization ; Penalty term ; Problem-based ; Projected gradient ; Single-step ; Statistical independence ; Synthetic data ; Factorization ; Signal processing ; Spectroscopy ; Algorithms
  7. Source: European Signal Processing Conference ; 2012 , Pages 2482-2486 ; 22195491 (ISSN) ; 9781467310680 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6334063&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6334063