The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization
Open accessAbstract
Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the$L_{1}$regularizer. Unfortunately, the$L_{1}$regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the$L_{1/2}$sparsity constraint, which we name$L_{1/2}$-NMF. The$L_{1/2}$regularizer not only induces sparsity but is also a better choice among$L_{q}(0 < q < 1)$regularizers. We propose an iterative estimation algorithm for$L_{1/2}$-NMF, which provides sparser and more accurate results than those delivered using the$L_{1}$norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods.
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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
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
He, W., Zhang, H., Zhang, L., & Shen, H. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2452812 · Cited by 667
Qian, Y., Ye, M., & Zhou, J. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2209657 · Cited by 344
Self-citationLow-Rank and Sparse Representation for Hyperspectral Image Processing: A review
Peng, J., Sun, W., Li, H.-C., Li, W., Meng, X., Ge, C., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3075491 · Cited by 285
Spectral Unmixing via Data-Guided Sparsity
Zhu, F., Wang, Y., Fan, B., Xiang, S., Meng, G., & Pan, C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2363423 · Cited by 276
Metadata
- Authors
- Qian, Y., Jia, S., Zhou, J., & Robles-Kelly, A.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-11
- Volume
- 49
- Issue
- 11
- Pages
- 4282-4297
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2032944446
- Cited by
- 598
- References
- 45
- FWCI
- 48.11
- Type
- journal article
- Language
- en
- Open access
- green
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- Qian, Y., Jia, S., Zhou, J., & Robles-Kelly, A. (2011). Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2011.2144605
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