The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing
Open accessAbstract
Convex optimization problems are common in hyperspectral unmixing. Examples are the constrained least squares (CLS) problem used to compute the fractional abundances in a linear mixture of known spectra, the constrained basis pursuit (CBP) to find sparse (i.e., with a small number of terms) linear mixtures of spectra, selected from large libraries, and the constrained basis pursuit denoising (CBPDN), which is a generalization of BP to admit modeling errors. In this paper, we introduce two new algorithms to efficiently solve these optimization problems, based on the alternating direction method of multipliers, a method from the augmented Lagrangian family. The algorithms are termed SUnSAL (sparse unmixing by variable splitting and augmented Lagrangian) and C-SUnSAL (constrained SUnSAL). C-SUnSAL solves the CBP and CBPDN problems, while SUnSAL solves CLS as well as a more general version thereof, called constrained sparse regression (CSR). C-SUnSAL and SUnSAL are shown to outperform off-the-shelf methods in terms of speed and accuracy.
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Rockafellar, R. T. (1970) · Princeton University Press eBooks · doi:10.1515/9781400873173 · Cited by 10,801
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,085
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationEckstein, J., & Bertsekas, D. P. (1992) · Mathematical Programming · doi:10.1007/bf01581204 · Cited by 2,791
Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549
A dual algorithm for the solution of nonlinear variational problems via finite element approximation
Gabay, D., & Mercier, B. (1976) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(76)90003-1 · Cited by 2,514
Glowinski, R., & Marroco, A. (1975) · Revue française d'automatique, informatique, recherche opérationnelle. Analyse numérique · doi:10.1051/m2an/197509r200411 · Cited by 1,105
Miao, L., & Qi, H. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.888466 · Cited by 921
A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data
Plaza, A., Martinez, P., Perez, R., & Plaza, J. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.820314 · Cited by 612
Spatial/spectral endmember extraction by multidimensional morphological operations
Plaza, A., Martinez, P., Perez, R., & Plaza, J. (2002) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2002.802494 · Cited by 517
Cited by
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationHyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Self-citationCollaborative Sparse Regression for Hyperspectral Unmixing
Iordache, M.-D., Bioucas-Dias, J. M., & Plaza, A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2240001 · Cited by 485
Self-citationRecent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning
Zhang, S., Su, Y., Xu, X., Li, J., Deng, C., & Plaza, A. (2020) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-030-38617-7_13 · Cited by 8
Metadata
- Authors
- Bioucas-Dias, J. M., & Figueiredo, M. A. T.
- Venue
- 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 1-4
- OpenAlex
- W2084252873
- Cited by
- 658
- References
- 21
- FWCI
- 32.26
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010). Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. https://doi.org/10.1109/whispers.2010.5594963
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