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Semisupervised local discriminant analysis for feature extraction in hyperspectral images

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Project
FWO project G.0123.08N “Spatial extension for classification of multispectral images” and SBO-IWT project Chameleon: Domain-specific Hyperspectral Imaging Systems for Relevant Industrial Applications.
Abstract
We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.
Keywords
feature extraction, hyperspectral remote sensing, semisupervised, Classification, ACCURACY, CLASSIFICATION, PATTERN-RECOGNITION, WEIGHTED FEATURE-EXTRACTION, DIMENSIONALITY REDUCTION, TANGENT-SPACE ALIGNMENT

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Citation

Please use this url to cite or link to this publication:

MLA
Liao, Wenzhi, Aleksandra Pizurica, Paul Scheunders, et al. “Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images.” Ed. Antonio Plaza. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.1 (2013): 184–198. Print.
APA
Liao, Wenzhi, Pizurica, A., Scheunders, P., Philips, W., & Pi, Y. (2013). Semisupervised local discriminant analysis for feature extraction in hyperspectral images. (A. Plaza, Ed.)IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 51(1), 184–198.
Chicago author-date
Liao, Wenzhi, Aleksandra Pizurica, Paul Scheunders, Wilfried Philips, and Youguo Pi. 2013. “Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images.” Ed. Antonio Plaza. Ieee Transactions on Geoscience and Remote Sensing 51 (1): 184–198.
Chicago author-date (all authors)
Liao, Wenzhi, Aleksandra Pizurica, Paul Scheunders, Wilfried Philips, and Youguo Pi. 2013. “Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images.” Ed. Antonio Plaza. Ieee Transactions on Geoscience and Remote Sensing 51 (1): 184–198.
Vancouver
1.
Liao W, Pizurica A, Scheunders P, Philips W, Pi Y. Semisupervised local discriminant analysis for feature extraction in hyperspectral images. Plaza A, editor. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2013;51(1):184–98.
IEEE
[1]
W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, “Semisupervised local discriminant analysis for feature extraction in hyperspectral images,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 51, no. 1, pp. 184–198, 2013.
@article{2962890,
  abstract     = {We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.},
  author       = {Liao, Wenzhi and Pizurica, Aleksandra and Scheunders, Paul  and Philips, Wilfried and Pi, Youguo},
  editor       = {Plaza, Antonio},
  issn         = {0196-2892},
  journal      = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
  keywords     = {feature extraction,hyperspectral remote sensing,semisupervised,Classification,ACCURACY,CLASSIFICATION,PATTERN-RECOGNITION,WEIGHTED FEATURE-EXTRACTION,DIMENSIONALITY REDUCTION,TANGENT-SPACE ALIGNMENT},
  language     = {eng},
  number       = {1},
  pages        = {184--198},
  title        = {Semisupervised local discriminant analysis for feature extraction in hyperspectral images},
  url          = {http://dx.doi.org/10.1109/TGRS.2012.2200106},
  volume       = {51},
  year         = {2013},
}

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