Double constrained NMF for hyperspectral unmixing

Xiaoqiang Lu, Hao Wu, Yuan Yuan

Research output: Contribution to journalArticlepeer-review

117 Scopus citations

Abstract

Given only the collected hyperspectral data, unmixing aims at obtaining the latent constituent materials and their corresponding fractional abundances. Recently, many nonnegative matrix factorization (NMF)-based algorithms have been developed to deal with this issue. Considering that the abundances of most materials may be sparse, the sparseness constraint is intuitively introduced into NMF. Although sparse NMF algorithms have achieved advanced performance in unmixing, the result is still susceptible to unstable decomposition and noise corruption. To reduce the aforementioned drawbacks, the structural information of the data is exploited to guide the unmixing. Since similar pixel spectra often imply similar substance constructions, clustering can explicitly characterize this similarity. Through maintaining the structural information during the unmixing, the resulting fractional abundances by the proposed algorithm can well coincide with the real distributions of constituent materials. Moreover, the additional clustering-based regularization term also lessens the interference of noise to some extent. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art algorithms.

Original languageEnglish
Article number6553069
Pages (from-to)2746-2758
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume52
Issue number5
DOIs
StatePublished - May 2014
Externally publishedYes

Keywords

  • Clustering-based regularization
  • hyperspectral unmixing
  • mixed pixel
  • nonnegative matrix factorization (NMF)

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