Dynamic Learning of SCRF for Feature Selection and Classification of Hyperspectral Imagery

  • Ping Zhong
  • Zhiming Qian
  • Runsheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


This paper investigates the feature selection and contextual classification of hyperspectral images through the sparse conditional random field (SCRF) model. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when SCRF selects a small feature subset, we develop a dynamic learning framework to train the SCRF. Under the piecewise training framework, the proposed dynamic learning method of SCRF can be implemented efficiently through separated dynamic sparse trainings of simple classifiers defined by corresponding potentials. Experiments on the real-world hyperspectral images attest to the effectiveness of the proposed method.


Conditional random field classification feature selection 


  1. 1.
    Chi, M., Bruzzone, L.: Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal. IEEE Trans. Geosci. Remote Sens. 45, 1870–1880 (2007)CrossRefGoogle Scholar
  2. 2.
    Muñoz-Marí, Bruzzone, L., Camps-Valls, G.: A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 45, 2683–2692 (2007)CrossRefGoogle Scholar
  3. 3.
    Ashish, D., McClendon, R.W., Hoogenboom, G.: Land-use classification of multispectral aerial images using artificial neural networks. Int. Jour. Remote Sens. 30, 1989–2004 (2009)CrossRefGoogle Scholar
  4. 4.
    Camps-Valls, G., Marsheva, T.V.B., Zhou, D.: Semi-Supervised Graph-Based Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. 45, 3044–3054 (2007)CrossRefGoogle Scholar
  5. 5.
    Zhong, P., Wang, R.: Modeling and Classifying Hyperspectral Imagery by CRFs with Sparse Higher Order Potentials. IEEE Trans. Geosci. Remote Sens. 49, 688–705 (2011)CrossRefGoogle Scholar
  6. 6.
    Zhong, P., Wang, R.: Learning conditional random fields for classification of hyperspectral images. IEEE Trans. Image Process. 19, 1890–1907 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning, pp. 282–289 (2001)Google Scholar
  8. 8.
    Zhong, P., Wang, R.: A multiple Conditional random fields ensemble model for urban area detection in remote sensing optical images. IEEE Trans. Geosci. Remote Sens. 45, 3978–3988 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhong, P., Wang, R.: Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery. IEEE Trans. Geosci. Remote Sens. 46, 4186–4197 (2008)CrossRefGoogle Scholar
  10. 10.
    Kumar, S.: Models for learning spatial interactions in natural images for context-based classification. PhD thesis. Carnegie Mellon University (2005)Google Scholar
  11. 11.
    Krishnapuram, B., Carin, L., Figueiredo, M.A.T., Hartemink, A.J.: Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Trans. Pattern Anal. Machine Intell. 27, 957–968 (2005)CrossRefGoogle Scholar
  12. 12.
    Ng, A.Y.: Feature selection, L1 vs. L2 regularization, and rotational invariance. In: International Conference on Machine Learning (2004)Google Scholar
  13. 13.
    Zhong, P., Zhang, P., Wang, R.: Dynamic learning of sparse multinomial logistic regression for feature selection and classification of hyperspectral data. IEEE Geosci. Remote Sens. Lett. 5, 280–284 (2008)CrossRefGoogle Scholar
  14. 14.
    Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. Jour. Comp. Vision. 81, 2–23 (2009)CrossRefGoogle Scholar
  15. 15.
    Landgrebe, D.A.: Signal Theory Methods in Multispectral Remote Sensing. Wiley, Hoboken (2003)CrossRefGoogle Scholar
  16. 16.
    Figueiredo, M.A.T.: Adaptive sparseness for supervised learning. IEEE Trans. Pattern Anal. Machine Intell. 25, 1150–1159 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ping Zhong
    • 1
  • Zhiming Qian
    • 1
  • Runsheng Wang
    • 1
  1. 1.ATR National Laboratory, School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina

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