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Hyperspectral unmixing using double reweighted sparse regression and total variation

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Abstract
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 l1 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.
Keywords
hyperspectral imaging, spectral unmixing

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MLA
Wang, Rui et al. “Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.3 (2017): 1146–1150. Print.
APA
Wang, Rui, Li, H.-C., Pizurica, A., Li, J., Plaza, A., & Emery, W. (2017). Hyperspectral unmixing using double reweighted sparse regression and total variation. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 14(3), 1146–1150.
Chicago author-date
Wang, Rui, Heng-Chao Li, Aleksandra Pizurica, Jun Li, Antonio Plaza, and William Emery. 2017. “Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.” Ieee Geoscience and Remote Sensing Letters 14 (3): 1146–1150.
Chicago author-date (all authors)
Wang, Rui, Heng-Chao Li, Aleksandra Pizurica, Jun Li, Antonio Plaza, and William Emery. 2017. “Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.” Ieee Geoscience and Remote Sensing Letters 14 (3): 1146–1150.
Vancouver
1.
Wang R, Li H-C, Pizurica A, Li J, Plaza A, Emery W. Hyperspectral unmixing using double reweighted sparse regression and total variation. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. IEEE; 2017;14(3):1146–50.
IEEE
[1]
R. Wang, H.-C. Li, A. Pizurica, J. Li, A. Plaza, and W. Emery, “Hyperspectral unmixing using double reweighted sparse regression and total variation,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol. 14, no. 3, pp. 1146–1150, 2017.
@article{8529655,
  abstract     = {{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 l1 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.}},
  author       = {{Wang, Rui and Li, Heng-Chao and Pizurica, Aleksandra and Li, Jun and Plaza, Antonio and Emery, William}},
  issn         = {{1545-598X}},
  journal      = {{IEEE GEOSCIENCE AND REMOTE SENSING LETTERS}},
  keywords     = {{hyperspectral imaging,spectral unmixing}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{1146--1150}},
  publisher    = {{IEEE}},
  title        = {{Hyperspectral unmixing using double reweighted sparse regression and total variation}},
  url          = {{http://dx.doi.org/10.1109/LGRS.2017.2700542}},
  volume       = {{14}},
  year         = {{2017}},
}

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