A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)


Improving the classification accuracy of remotely sensed data is of paramount interest for science and defense applications. In this paper, we investigate deep learning architectures (DLAs), whose popularity has grown recently due to the discovery of efficient algorithms to train them, one of which, unsupervised pre-training, seeks to initialize the learned model in a way that greatly encourages efficient supervised learning. We propose a structure for a DLA, the deep belief network (DBN), suitable for the classification of remotely-sensed hyperspectral data. To arrive at this structure, we first study the role of the DBN’s width and the duration of pre-training in the learning of features used for the multiclass discrimination of spectral data. We then study the effect of exploiting joint spectral-spatial information. The support vector machine (SVM) is used as a baseline to determine that the proposed method is feasible, offering consistently high classification accuracies in comparison.


Deep Belief Network (DBN) Hyperspectral Remote Sensing Data Deep Learning Architecture (DLA) Restricted Boltzmann Machines (RBM) Support Vector Machine Kernel Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by NASA EPSCoR under cooperative agreement No. NNX10AR89A.


  1. 1.
    Bruce, L., Koger, C., Li, J.: Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans. Geosci. Remote Sens. 40, 2331–2338 (2002)CrossRefGoogle Scholar
  2. 2.
    Harsanyi, J., Chang, C.I.: Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 32, 779–785 (1994)CrossRefGoogle Scholar
  3. 3.
    Kang, X., Li, S., Benediktsson, J.: Spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 52, 2666–2677 (2014)CrossRefGoogle Scholar
  4. 4.
    Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004)CrossRefGoogle Scholar
  5. 5.
    Pal, M., Foody, G.: Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 48, 2297–2307 (2010)CrossRefGoogle Scholar
  6. 6.
    Li, J., Bioucas-Dias, J., Plaza, A.: Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Trans. Geosci. Remote Sens. 49, 3947–3960 (2011)CrossRefGoogle Scholar
  7. 7.
    Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci. Remote Sens. Lett. 10, 318–322 (2013)CrossRefGoogle Scholar
  8. 8.
    Li, J., Bioucas-Dias, J., Plaza, A.: Spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Trans. Geosci. Remote Sens. 51, 844–856 (2013)CrossRefGoogle Scholar
  9. 9.
    Bernard, K., Tarabalka, Y., Angulo, J., Chanussot, J., Benediktsson, J.: Spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach. IEEE Trans. Image Process. 21, 2008–2021 (2012)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Larochelle, H., Erhan, D., Courville, A., Bergstra, J., Bengio, Y.: An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 473–480. ACM, New York (2007)Google Scholar
  12. 12.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives. CoRR abs/1206.5538 (2012)
  14. 14.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)zbMATHCrossRefGoogle Scholar
  15. 15.
    Bengio, Y., Lecun, Y., Operationnelle, D.D.E.R., Montreal, U.D.: Scaling learning algorithms towards AI. In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) Large-Scale Kernel Machines. MIT Press, Cambridge (2007)Google Scholar
  16. 16.
    Lin, Z., Chen, Y., Zhao, X., Wang, G.: Spectral-spatial classification of hyperspectral image using autoencoders. In: 2013 9th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5 (2013)Google Scholar
  17. 17.
    Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2094–2107 (2014)CrossRefGoogle Scholar
  18. 18.
    Chen, Y., Zhao, X., Jia, X.: Spectral-spatial classification of hyperspectral data based on deep belief network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 2381–2392 (2015)CrossRefGoogle Scholar
  19. 19.
    Le Roux, N., Bengio, Y.: Representational power of restricted boltzmann machines and deep belief networks. Neural Comput. 20, 1631–1649 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Montral, U.D., Qubec, M.: Greedy layer-wise training of deep networks. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) NIPS. MIT Press, Cambridge (2007)Google Scholar
  21. 21.
    Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. CoRR abs/1206.5533 (2012)
  22. 22.
    Serpico, S., Bruzzone, L.: A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans. Geosci. Remote Sens. 39, 1360–1367 (2001)CrossRefGoogle Scholar
  23. 23.
    Chang, C.I., Du, Q., Sun, T.L., Althouse, M.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37, 2631–2641 (1999)CrossRefGoogle Scholar
  24. 24.
    Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: IGARSS, pp. 1771–1800 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Nevada, Las VegasLas VegasUSA

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