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Unsupervised spectral sub-feature learning for hyperspectral image classification

Viktor Slavkovikj, Steven Verstockt UGent, Wesley De Neve UGent, Sofie Van Hoecke UGent and Rik Van de Walle UGent (2016) INTERNATIONAL JOURNAL OF REMOTE SENSING. 37(2). p.309-326
abstract
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
NONLINEAR DIMENSIONALITY REDUCTION, INDEPENDENT COMPONENT ANALYSIS, LINEAR DISCRIMINANT-ANALYSIS, FRAMEWORK, SELECTION, SVM, CONSTRAINT, FACE RECOGNITION, FEATURE-EXTRACTION
journal title
INTERNATIONAL JOURNAL OF REMOTE SENSING
Int. J. Remote Sens.
volume
37
issue
2
pages
309 - 326
publisher
TAYLOR & FRANCIS LTD
place of publication
ABINGDON
Web of Science type
Article
Web of Science id
000368724700003
JCR category
IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
JCR impact factor
1.724 (2016)
JCR rank
13/26 (2016)
JCR quartile
2 (2016)
ISSN
0143-1161
DOI
10.1080/01431161.2015.1125554
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
7087431
handle
http://hdl.handle.net/1854/LU-7087431
date created
2016-02-16 15:53:41
date last changed
2017-01-02 09:55:17
@article{7087431,
  abstract     = {Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods.},
  author       = {Slavkovikj, Viktor and Verstockt, Steven and De Neve, Wesley and Van Hoecke, Sofie and Van de Walle, Rik},
  issn         = {0143-1161},
  journal      = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
  keyword      = {NONLINEAR DIMENSIONALITY REDUCTION,INDEPENDENT COMPONENT ANALYSIS,LINEAR DISCRIMINANT-ANALYSIS,FRAMEWORK,SELECTION,SVM,CONSTRAINT,FACE RECOGNITION,FEATURE-EXTRACTION},
  language     = {eng},
  number       = {2},
  pages        = {309--326},
  publisher    = {TAYLOR \& FRANCIS LTD},
  title        = {Unsupervised spectral sub-feature learning for hyperspectral image classification},
  url          = {http://dx.doi.org/10.1080/01431161.2015.1125554},
  volume       = {37},
  year         = {2016},
}

Chicago
Slavkovikj, Viktor, Steven Verstockt, Wesley De Neve, Sofie Van Hoecke, and Rik Van de Walle. 2016. “Unsupervised Spectral Sub-feature Learning for Hyperspectral Image Classification.” International Journal of Remote Sensing 37 (2): 309–326.
APA
Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., & Van de Walle, R. (2016). Unsupervised spectral sub-feature learning for hyperspectral image classification. INTERNATIONAL JOURNAL OF REMOTE SENSING, 37(2), 309–326.
Vancouver
1.
Slavkovikj V, Verstockt S, De Neve W, Van Hoecke S, Van de Walle R. Unsupervised spectral sub-feature learning for hyperspectral image classification. INTERNATIONAL JOURNAL OF REMOTE SENSING. ABINGDON: TAYLOR & FRANCIS LTD; 2016;37(2):309–26.
MLA
Slavkovikj, Viktor, Steven Verstockt, Wesley De Neve, et al. “Unsupervised Spectral Sub-feature Learning for Hyperspectral Image Classification.” INTERNATIONAL JOURNAL OF REMOTE SENSING 37.2 (2016): 309–326. Print.