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Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification

Renbo Luo (UGent) , Wenzhi Liao (UGent) and Youguo Pi
(2012) TELKOMNIKA. 10(5). p.1051-1056
Author
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Abstract
A novel discriminative supervised neighborhood preserving embedding (DSNPE) method is proposed for feature extraction in classifying hyperspectral remote sensing imagery. DSNPE can preserve the local manifold structure and the neighborhood structure. What’s more, for each data point, DSNPE aims at pulling the neighboring points with the same class label towards it as near as possible, while simultaneously pushing the neighboring points with different labels apart from it as far as possible. Experimental results on two real hyperspectral image datasets are reported to illustrate the performance of DSNPE and to compare it with a few competing methods.
Keywords
Classification, hyperspectral image, feature extraction

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Citation

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

MLA
Luo, Renbo, et al. “Discriminative Supervised Neighborhood Preserving Embedding Feature Extraction for Hyperspectral-Image Classification.” TELKOMNIKA, edited by Tole Sutikno, vol. 10, no. 5, 2012, pp. 1051–56.
APA
Luo, R., Liao, W., & Pi, Y. (2012). Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification. TELKOMNIKA, 10(5), 1051–1056.
Chicago author-date
Luo, Renbo, Wenzhi Liao, and Youguo Pi. 2012. “Discriminative Supervised Neighborhood Preserving Embedding Feature Extraction for Hyperspectral-Image Classification.” Edited by Tole Sutikno. TELKOMNIKA 10 (5): 1051–56.
Chicago author-date (all authors)
Luo, Renbo, Wenzhi Liao, and Youguo Pi. 2012. “Discriminative Supervised Neighborhood Preserving Embedding Feature Extraction for Hyperspectral-Image Classification.” Ed by. Tole Sutikno. TELKOMNIKA 10 (5): 1051–1056.
Vancouver
1.
Luo R, Liao W, Pi Y. Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification. Sutikno T, editor. TELKOMNIKA. 2012;10(5):1051–6.
IEEE
[1]
R. Luo, W. Liao, and Y. Pi, “Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification,” TELKOMNIKA, vol. 10, no. 5, pp. 1051–1056, 2012.
@article{5840545,
  abstract     = {{A  novel  discriminative  supervised  neighborhood  preserving  embedding  (DSNPE)  method  is proposed for feature extraction in classifying hyperspectral remote sensing imagery. DSNPE can preserve the  local  manifold  structure  and  the  neighborhood  structure.  What’s  more,  for  each  data  point,  DSNPE aims  at  pulling  the  neighboring  points  with  the  same  class  label  towards  it  as  near  as  possible,  while simultaneously  pushing  the  neighboring  points  with different  labels  apart  from  it  as  far  as  possible. Experimental results on two real hyperspectral image datasets are reported to illustrate the performance of DSNPE and to compare it with a few competing methods.}},
  author       = {{Luo, Renbo and Liao, Wenzhi and Pi, Youguo}},
  editor       = {{Sutikno, Tole}},
  issn         = {{2302-4046}},
  journal      = {{TELKOMNIKA}},
  keywords     = {{Classification,hyperspectral image,feature extraction}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{1051--1056}},
  title        = {{Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification}},
  url          = {{http://dx.doi.org/10.11591/telkomnika.v10i5.1346}},
  volume       = {{10}},
  year         = {{2012}},
}

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