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Feature extraction for hyperspectral images based on semi-supervised local linear discriminant analysis

Wenzhi Liao UGent, Aleksandra Pizurica UGent, Wilfried Philips UGent and Youguo Pi (2011) Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings. p.401-404
abstract
We propose a novel semi-supervised local discriminant analysis (SELD) method for feature extraction in hyperspectral remote sensing imagery. The proposed method combines a supervised method (Linear Discriminant Analysis (LDA)) and an unsupervised method (Neighborhood Preserving Embedding (NPE)) without any free parameters. The underlying idea is to design optimal projection vectors, which can discover the global discriminant structure of the available labeled samples while preserving the local neighborhood spatial structure of the unlabeled samples. Furthermore, in our approach the number of extracted feature bands is no longer limited by the number of classes, which is a disadvantage of LDA. Experimental results demonstrate that the proposed method outperforms consistently other related semi-supervised methods and that it is also much more stable when the percentage of the labeled samples changes.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (conferencePaper)
publication status
published
subject
keyword
feature extraction, Hyperspectral remote sensing, classification
in
Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings
editor
Uwe Stilla, P Gamba, C Juergens and D Maktav
pages
401 - 404
publisher
IEEE
conference name
Joint Urban Remote Sensing Event (JURSE - 2011)
conference location
Munich, Germany
conference start
2011-04-11
conference end
2011-04-13
Web of Science type
Conference Paper
Web of Science id
11975963
ISBN
9781424486571
project
FWO
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1216543
handle
http://hdl.handle.net/1854/LU-1216543
alternative location
http://archive.ugent.be/person/000091851926
date created
2011-05-03 19:51:27
date last changed
2016-12-19 15:35:09
@inproceedings{1216543,
  abstract     = {We propose a novel semi-supervised local discriminant analysis (SELD) method for feature extraction in hyperspectral remote sensing imagery. The proposed method combines a supervised method (Linear Discriminant Analysis (LDA)) and an unsupervised method (Neighborhood Preserving Embedding (NPE)) without any free parameters. The underlying idea is to design optimal projection vectors, which can discover the global discriminant structure of the available labeled samples while preserving the local neighborhood spatial structure of the unlabeled samples. Furthermore, in our approach the number of extracted feature bands is no longer limited by the number of classes, which is a disadvantage  of LDA. Experimental  results demonstrate that the proposed method outperforms consistently other related semi-supervised methods and that it is also much more stable when the percentage of the labeled samples changes.},
  author       = {Liao, Wenzhi and Pizurica, Aleksandra and Philips, Wilfried and Pi, Youguo},
  booktitle    = {Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings},
  editor       = {Stilla, Uwe  and Gamba, P and Juergens, C and Maktav, D},
  isbn         = {9781424486571},
  keyword      = {feature extraction,Hyperspectral remote sensing,classification},
  language     = {eng},
  location     = {Munich, Germany},
  pages        = {401--404},
  publisher    = {IEEE},
  title        = {Feature extraction for hyperspectral images based on semi-supervised local linear discriminant analysis},
  url          = {http://archive.ugent.be/person/000091851926},
  year         = {2011},
}

Chicago
Liao, Wenzhi, Aleksandra Pizurica, Wilfried Philips, and Youguo Pi. 2011. “Feature Extraction for Hyperspectral Images Based on Semi-supervised Local Linear Discriminant Analysis.” In Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings, ed. Uwe Stilla, P Gamba, C Juergens, and D Maktav, 401–404. IEEE.
APA
Liao, Wenzhi, Pizurica, A., Philips, W., & Pi, Y. (2011). Feature extraction for hyperspectral images based on semi-supervised local linear discriminant analysis. In U. Stilla, P. Gamba, C. Juergens, & D. Maktav (Eds.), Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings (pp. 401–404). Presented at the Joint Urban Remote Sensing Event (JURSE - 2011), IEEE.
Vancouver
1.
Liao W, Pizurica A, Philips W, Pi Y. Feature extraction for hyperspectral images based on semi-supervised local linear discriminant analysis. In: Stilla U, Gamba P, Juergens C, Maktav D, editors. Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings. IEEE; 2011. p. 401–4.
MLA
Liao, Wenzhi, Aleksandra Pizurica, Wilfried Philips, et al. “Feature Extraction for Hyperspectral Images Based on Semi-supervised Local Linear Discriminant Analysis.” Joint Urban Remote Sensing Event (JURSE - 2011), Proceedings. Ed. Uwe Stilla et al. IEEE, 2011. 401–404. Print.