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An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image

Renbo Luo (UGent) , Wenzhi Liao (UGent) , Wilfried Philips (UGent) and Youguo Pi
Author
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Abstract
We propose an improved semi-supervised local discriminant analysis (ISELD) for feature extraction of hyperspectral image in this paper. The proposed ISELD method aims to find a projection which can preserve local neighborhood information and maximize the class discrimination of the data. Compared to the previous SELD, the proposed ISELD better models the correlation of labeled and unlabeled samples. Experimental results on an ROSIS urban hyperspectral image are encouraging. Compared to some recent feature extraction methods, our approach has more than 2% improvements as the training sample size changes.
Keywords
DIMENSIONALITY REDUCTION, CLASSIFICATION

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Citation

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

MLA
Luo, Renbo, et al. “An Improved Semi-Supervised Local Discriminant Analysis for Feature Extraction of Hyperspectal Image.” 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE), 2015.
APA
Luo, R., Liao, W., Philips, W., & Pi, Y. (2015). An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image. In 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE). Lausanne, Switzerland.
Chicago author-date
Luo, Renbo, Wenzhi Liao, Wilfried Philips, and Youguo Pi. 2015. “An Improved Semi-Supervised Local Discriminant Analysis for Feature Extraction of Hyperspectal Image.” In 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE).
Chicago author-date (all authors)
Luo, Renbo, Wenzhi Liao, Wilfried Philips, and Youguo Pi. 2015. “An Improved Semi-Supervised Local Discriminant Analysis for Feature Extraction of Hyperspectal Image.” In 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE).
Vancouver
1.
Luo R, Liao W, Philips W, Pi Y. An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image. In: 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE). 2015.
IEEE
[1]
R. Luo, W. Liao, W. Philips, and Y. Pi, “An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image,” in 2015 JOINT URBAN REMOTE SENSING EVENT (JURSE), Lausanne, Switzerland, 2015.
@inproceedings{5898049,
  abstract     = {{We propose an improved semi-supervised local discriminant analysis (ISELD) for feature extraction of hyperspectral image in this paper. The proposed ISELD method aims to find a projection which can preserve local neighborhood information and maximize the class discrimination of the data. Compared to the previous SELD, the proposed ISELD better models the correlation of labeled and unlabeled samples. Experimental results on an ROSIS urban hyperspectral image are encouraging. Compared to some recent feature extraction methods, our approach has more than 2% improvements as the training sample size changes.}},
  author       = {{Luo, Renbo and Liao, Wenzhi and Philips, Wilfried and Pi, Youguo}},
  booktitle    = {{2015 JOINT URBAN REMOTE SENSING EVENT (JURSE)}},
  isbn         = {{978-1-4799-6652-3}},
  keywords     = {{DIMENSIONALITY REDUCTION,CLASSIFICATION}},
  language     = {{eng}},
  location     = {{Lausanne, Switzerland}},
  pages        = {{4}},
  title        = {{An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image}},
  year         = {{2015}},
}

Web of Science
Times cited: