Application of Deep Belief Network to Land Cover Classification Using Hyperspectral Images

  • Bulent Ayhan
  • Chiman KwanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10261)


This paper summarizes some preliminary results of applying deep belief network (DBN) to land classification using hyperspectral images. The performance of DBN is then compared with several conventional classification approaches. A fusion approach is also proposed to combine spatial and spectral information in the classification process. Actual hyperspectral image data were used in our investigations. Based on the particular data and experiments, it was found that DBN has slightly better classification performance if only spectral information is used and has slightly inferior performance than a conventional method if both spatial and spectral information are used.


Deep learning DBN SVM SAM Hyperspectral image Land classification 


  1. 1.
    Liang, S., Li, X., Wang, J.: Advanced Remote Sensing: Terrestrial Information Extraction and Applications. Academic Press, Cambridge (2012)Google Scholar
  2. 2.
    Zhou, J., Kwan, C., Ayhan, B., Eismann, M.: A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images. IEEE Trans. Geosci. Remote Sens. 54(11), 6497–6504 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhou, J., Kwan, C., Budavari, B.: Hyperspectral image super-resolution: a hybrid color mapping approach. SPIE J. Appl. Remote Sens. 10, 035024 (2016)CrossRefGoogle Scholar
  4. 4.
    Ayhan, B., Kwan, C.: On the use of radiance domain for burn scar detection under varying atmospheric illumination conditions and viewing geometry. J. Sig. Image Video Process. 11, 605–612 (2016)CrossRefGoogle Scholar
  5. 5.
    Kwan, C., Choi, J.H., Chan, S., Zhou, J., Budavari, B.: Resolution enhancement for hyperspectral images: a super-resolution and fusion approach. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, New Orleans (2017)Google Scholar
  6. 6.
    Nguyen, D., Tran, T., Kwan, C., Ayhan, B.: Endmember extraction in hyperspectral images using l-1 minimization and linear complementary programming. In: SPIE, vol. 7695 (2010)Google Scholar
  7. 7.
    Li, S., Wang, W., Qi, H., Ayhan, B., Kwan, C., Vance, S.: Low-rank tensor decomposition based anomaly detection for hyperspectral imagery. In: IEEE International Conference on Image Processing (ICIP), Quebec City, Canada (2015)Google Scholar
  8. 8.
    Qu, Y., Guo, R., Wang, W., Qi, H., Ayhan, B., Kwan, C., Vance, S.: Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition. In: International Geoscience and Remote Sensing Symposium (IGARSS), Beijing (2016)Google Scholar
  9. 9.
    Lee, C.M., Cable, M.L., Hook, S.J., Green, R.O., Ustin, S.L., Mandl, D.J., Middleton, E.M.: An introduction to the NASA hyperspectral infrared imager (HyspIRI) mission and preparatory activities. Remote Sens. Environ. 167, 6–19 (2015)CrossRefGoogle Scholar
  10. 10.
    Lecun, Y., Ranzato, M.: Deep learning tutorial. In: 30th International Conference on Machine Learning, Atlanta (2013)Google Scholar
  11. 11.
    Qian, T., Li, X., Ayhan, B., Xu, R., Kwan, C., Griffin, T.: Application of support vector machines to vapor detection and classification for environmental monitoring of spacecraft. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1216–1222. Springer, Heidelberg (2006). doi: 10.1007/11760191_177 CrossRefGoogle Scholar
  12. 12.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  13. 13.
    Kwan, C., Ayhan, B., Chen, G., Chang, C., Wang, J., Ji, B.: A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents. IEEE Trans. Geosci. Remote Sens. 44, 409–419 (2006)CrossRefGoogle Scholar
  14. 14.
    Palm, R.B.: Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmark (2012)Google Scholar
  15. 15.
    Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(6), 2094–2107 (2014)CrossRefGoogle Scholar
  16. 16.
    Qian, T., Xu, R., Kwan, C., Linnell, B., Young, R.: Toxic vapor classification and concentration estimation for space shuttle and international space station. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 543–551. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28647-9_90 CrossRefGoogle Scholar
  17. 17.
    Ayhan, B., Kwan, C., Li, X., Trang, A.: Airborne detection of land mines using mid-wave infrared (MWIR) and laser-illuminated-near infrared images with the RXD hyperspectral anomaly detection method. In: Fourth International Workshop on Pattern Recognition in Remote Sensing, Hong Kong (2006)Google Scholar
  18. 18.
    Ayhan, B., Kwan, C., Vance, S.: On the use of a linear spectral unmixing technique for concentration estimation of APXS spectrum. J. Multi. Eng. Sci. Technol. 2(9), 2469–2474 (2015)Google Scholar
  19. 19.
    Zhou, J., Kwan, C.: Fast anomaly detection algorithms for hyperspectral images. J. Mult. Eng. Sci. Technol. 2(9), 2521–2525 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Signal Processing, Inc.RockvilleUSA

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