Hyperspectral Image Classification Using Semi-supervised Random Forest

  • Sunit Kumar AdhikaryEmail author
  • Sourish Gunesh Dhekane
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


In this paper, a hyperspectral image classification technique is proposed using semi-supervised random forest (SSRF). Robust node splitting in the random forest requires enormous training data, which is scarce in remote sensing applications. In order to overcome this drawback, we propose utilizing unlabeled data in conjunction with labeled data to assist the splitting process. Moreover, in order to tackle the curse of dimensionality associated with a hyperspectral image, we explore nonnegative matrix factorization (NMF) to remove redundant information. Experimental results confirm the efficacy of the proposed method.


Hyperspectral imaging Semi-supervised learning Random forest Nonnegative matrix factorization 



The authors thank Prof. Gamba for providing the University of Pavia dataset.


  1. 1.
    Mather PM, Koch M (2010) Computer processing of remotely-sensed images, 4th edn. Wiley, New YorkGoogle Scholar
  2. 2.
    Baraldi A, Bruzzone L, Blonda P (2005) Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Trans Geosci Remote Sens 43:857–873CrossRefGoogle Scholar
  3. 3.
    Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Trans Inf Theory 14:55–63CrossRefGoogle Scholar
  4. 4.
    Bruzzone L, Chi M, Marconcini M (2006) A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 44:3363–3373CrossRefGoogle Scholar
  5. 5.
    Gomez-Chova L, Camps-Valls G, Munnoz-Mari J, Calpe J (2008) Semisupervised image classification with Laplacian support vector machines. IEEE Geosci Remote Sens Lett 5:336–340CrossRefGoogle Scholar
  6. 6.
    Camps-Valls G, Bandos TV, Zhou D (2007) Semi-supervised graph-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 45:3044–3054CrossRefGoogle Scholar
  7. 7.
    Ratle F, Camps-Valls G, Weston J (2010) Semisupervised neural networks for efficient hyperspectral image classification. IEEE Trans Geosci Remote Sens 48(5):2271–2282CrossRefGoogle Scholar
  8. 8.
    Zhang T, Oles F (2000) A probability analysis on the value of unlabeled data for classification problems. In: 17th International conference on machine learning. ACM Press, California, pp 1191–1198Google Scholar
  9. 9.
    Lee DD, Seung S (2000) Algorithms for non-negative matrix factorization. In: Neural information processing systems. MIT Press, Denver, pp 556–562Google Scholar
  10. 10.
    Breiman L (1996) Bagging predictors. Mach Learn 26:123–140zbMATHGoogle Scholar
  11. 11.
    Liu X, Song M, Tao D, Liu Z, Zhang L, Chen C, Bu J (2013) Semi-supervised node splitting for random forest construction. In: Conference on computer vision and pattern recognition. IEEE Press, Oregon, pp 492–499Google Scholar
  12. 12.
    Silverman B (1986) Density estimation for statistics and data analysis. Chapman and Hall/CRC, LondonCrossRefGoogle Scholar
  13. 13.
    Criminisi A, Shotton J, Konukoglu E (2011) Decision forests for classification, regression, density estimation, mainfold learning and semi-supervised learning. Technical report, Microsoft ResearchGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sunit Kumar Adhikary
    • 1
    Email author
  • Sourish Gunesh Dhekane
    • 1
  1. 1.Indian Institute of Information Technology GuwahatiGuwahatiIndia

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