Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

Rui Wang, Heng Chao Li, Aleksandra Pizurica, Jun Li, Antonio Plaza, William J. Emery

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial-contextual information. As such, the simultaneous utilization of both double reweighted $l-{1}$ minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.

Original languageEnglish
Article number7935525
Pages (from-to)1146-1150
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number7
StatePublished - Jul 2017
Externally publishedYes


  • Double weights
  • hyperspectral unmixing
  • sparse regression
  • total variation (TV)


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