The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Vertex component analysis: a fast algorithm to unmix hyperspectral data
Abstract
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
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Cites
Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549
Bateson, C. A., Asner, G. P., & Wessman, C. A. (2000) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.841987 · Cited by 382
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
Hyperspectral 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-citationGhamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing
Jia, S., & Qian, Y. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2002882 · Cited by 369
Metadata
- Authors
- Nascimento, J. M. P., & Dias, J. M. B.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2005-04
- Volume
- 43
- Issue
- 4
- Pages
- 898-910
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2157321686
- Cited by
- 2,669
- References
- 2
- FWCI
- 44.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nascimento, J. M. P., & Dias, J. M. B. (2005). Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2005.844293
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