The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Hyperspectral Subspace Identification
Abstract
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549
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-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
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation
Xu, Y., Wu, Z., Li, J., Plaza, A., & Wei, Z. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2493201 · Cited by 602
Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning
Li, J., Bioucas-Dias, J. M., & Plaza, A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2128330 · Cited by 417
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Metadata
- Authors
- Bioucas-Dias, J. M., & Nascimento, J. M. P.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2008-08
- Volume
- 46
- Issue
- 8
- Pages
- 2435-2445
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2070424424
- Cited by
- 1,099
- References
- 3
- FWCI
- 81.71
- Type
- journal article
- Language
- en
- Open access
- closed
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