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Unsupervised spectral sub-feature learning for hyperspectral image classification

Viktor Slavkovikj (UGent) , Steven Verstockt (UGent) , Wesley De Neve (UGent) , Sofie Van Hoecke (UGent) and Rik Van de Walle (UGent)
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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.
Keywords
NONLINEAR DIMENSIONALITY REDUCTION, INDEPENDENT COMPONENT ANALYSIS, LINEAR DISCRIMINANT-ANALYSIS, FRAMEWORK, SELECTION, SVM, CONSTRAINT, FACE RECOGNITION, FEATURE-EXTRACTION

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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.
@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},
  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},
}

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