Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis

  • Paweł Ksieniewicz
  • Bartosz KrawczykEmail author
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9714)


Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper we introduce a novel pipeline for feature extraction and classification of hyperspectral images. To obtain a compressed representation we propose to extract a set of statistical-based properties from these images. This allows for embedding feature space into fourteen channels, obtaining a significant dimensionality reduction. These features are used as an input for the ensemble learning based on minimal-distance classifiers. We introduce a novel method for forming ensembles simple one dimensional classifiers. They are constructed independently on a low-dimensional representation - a single classifier for each extracted feature. Then a voting procedure is being used to obtain the final decision. Extensive experiments carried on a number of benchmarks images prove that using proposed feature extraction and ensemble of simple classifiers can offer a significant improvement in terms of classification accuracy when compared to state-of-the-art methods.


Ensemble learning Hyperspectral imaging Computer vision Feature extraction Dimensionality reduction Image classification 



This was supported in part by the statutory funds of Department of Systems and Computer Networks, Wrocław University of Technology and by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264.

All experiments were carried out using computer equipment sponsored by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement (


  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv F test for comparing supervised classification learning algorithms. Neural Comput. 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  2. 2.
    Ayerdi, B., Graña, M.: Hyperspectral image nonlinear unmixing and reconstruction by ELM regression ensemble. Neurocomputing 174, 299–309 (2016)CrossRefGoogle Scholar
  3. 3.
    Cyganek, B.: An analysis of the road signs classification based on the higher-order singular value decomposition of the deformable pattern tensors. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part II. LNCS, vol. 6475, pp. 191–202. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Hayes, M.H., Miller, S.N., Murphy, M.A.: High-resolution landcover classification using random forest. Remote Sens. Lett. 5(2), 112–121 (2014)CrossRefGoogle Scholar
  5. 5.
    Krawczyk, B., Ksieniewicz, P., Woźniak, M.: Hyperspectral image analysis based on color channels and ensemble classifier. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 274–284. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Ksieniewicz, P., Jankowski, D., Ayerdi, B., Jackowski, K., Graña, M., Woźniak, M.: A novel hyperspectral segmentation algorithm - concept and evaluation. Logic J. IGPL 23(1), 105–120 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, G.: Investigation of random subspace and random forest regression models using data with injected noise. In: Graña, M., Toro, C., Howlett, R.J., Jain, L.C. (eds.) KES 2012. LNCS, vol. 7828, pp. 1–10. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Li, S., Qiu, J., Yang, X., Liu, H., Wan, D., Zhu, Y.: A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search. Eng. Appl. AI 27, 241–250 (2014)CrossRefGoogle Scholar
  9. 9.
    Lin, D., Xu, X.: A novel method of feature extraction and fusion and its application in satellite images classification. Remote Sens. Lett. 6(9), 687–696 (2015)CrossRefGoogle Scholar
  10. 10.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  11. 11.
    Wei, W., Zhang, Y., Tian, C.: Latent subclass learning-based unsupervised ensemble feature extraction method for hyperspectral image classification. Remote Sens. Lett. 6(4), 257–266 (2015)CrossRefGoogle Scholar
  12. 12.
    Willett, R.M., Duarte, M.F., Davenport, M.A., Baraniuk, R.G.: Sparsity and structure in hyperspectral imaging : sensing, reconstruction, and target detection. IEEE Signal Process. Mag. 31(1), 116–126 (2014)CrossRefGoogle Scholar
  13. 13.
    Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)CrossRefGoogle Scholar
  14. 14.
    Yuan, Y., Lv, H., Lu, X.: Semi-supervised change detection method for multi–temporal hyperspectral images. Neurocomputing 148, 363–375 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Paweł Ksieniewicz
    • 1
  • Bartosz Krawczyk
    • 1
    Email author
  • Michał Woźniak
    • 1
  1. 1.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland

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