Spectral Band Selection Using Binary Gray Wolf Optimizer and Signal to Noise Ration Measure

  • Seyyid Ahmed MedjahedEmail author
  • Mohammed Ouali
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)


In remote sensing, spectral band selection has been a primordial step to improve the classification of hyperspectral images. It aims at finding the most important information from a set of bands by eliminating the irrelevant, noisy, and highly correlated bands. In this paper, the band selection problem is regarded as a combinatorial optimization problem. We propose a new band selection approach for hyperspectral image classification based on the Gray Wolf Optimizer (GWO) which is a new meta-heuristic that simulate the hunting process of gray wolf in nature. A new binary version of GWO based on transfer function is proposed. In addition, a new fitness function is designed using two terms: the first term is the SVM classifier and the second term of the fitness function is SNR measure (Signal to Noise Ration) which measures the capacity of discrimination. The proposed approach is benchmarked on three hyperspectral images widely used in band selection and hyperspectral images classification. The experimental results show that this approach is suitable to the challenging problem of spectral band selection and provides a higher classification accuracy rate compared to the other band selection methods.


Binary Gray Wolf Optimizer Support vector machine Spectral band selection Hyperspectral image classification Feature selection 


  1. 1.
    Medjahed, S.A., Ouali, M., Saadi, T.A., Benyettou, A.: An optimization-based framework for feature selection and parameters determination of SVMs. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 7(5), 1–9 (2015)Google Scholar
  2. 2.
    Medjahed, S.A., Saadi, T.A., Benyettou, A., Ouali, M.: Gray wolf optimizer for hyperspectral band selection. Appl. Soft Comput. 40, 178–186 (2016)CrossRefGoogle Scholar
  3. 3.
    Medjahed, S.A., Ouali, M.: Band selection based on optimization approach for hyperspectral image classification. Egypt. J. Remote. Sens. Space Sci. (2018)Google Scholar
  4. 4.
    Liu, H., Yang, S., Gou, S., Liu, S., Jiao, L.: Terrain classification based on spatial multi-attribute graph using polarimetric SAR data. Appl. Soft Comput. 68, 24–38 (2018)CrossRefGoogle Scholar
  5. 5.
    Boedihardjo, A.P., Lu, C.T., Chen, F.: Fast adaptive kernel density estimator for data streams. Knowl. Inf. Syst. 42(2) (2015)CrossRefGoogle Scholar
  6. 6.
    Zhang, Q., Tian, Y., Yang, Y., Pan, C.: Automatic spatialspectral feature selection for hyperspectral image via discriminative sparse multimodal learning. IEEE Trans. Geosci. Remote Sens. 53(1) (2015)Google Scholar
  7. 7.
    Wang, S., Pedrycz, W., Zhu, Q., Zhu, W.: Subspace learning for unsupervised feature selection via matrix factorization. Pattern Recognit. 48(1) (2015)CrossRefGoogle Scholar
  8. 8.
    Zhu, P., Zuo, W., Zhang, L., Hu, Q., Shiu, S.C.: Unsupervised feature selection by regularized self-representation. Pattern Recognit. 48(2) (2015)CrossRefGoogle Scholar
  9. 9.
    Wang, S., Pedrycz, W., Zhu, Q., Zhu, W.: Unsupervised feature selection via maximum projection and minimum redundancy. Knowl. Based Syst. 75(1) (2015)CrossRefGoogle Scholar
  10. 10.
    Lee, J., Kim, D.W.: Mutual information-based multi-label feature selection using interaction information. Expert. Syst. Appl. 42(4) (2015)CrossRefGoogle Scholar
  11. 11.
    Lewis, D.D.: Feature selection and feature extraction for text categorization. In: Proceedings of Speech and Natural Language Workshop, pp. 212–217. Morgan Kaufmann (1992)Google Scholar
  12. 12.
    Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5(4), 537–550 (1994)CrossRefGoogle Scholar
  13. 13.
    Yang, H., Moody, J.: Feature selection based on joint mutual information. In: Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis, pp. 22–25 (1999)Google Scholar
  14. 14.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 1226–1238 (2005)Google Scholar
  15. 15.
    Brown, G., Pocock, A., Zhao, M.J., Luj, M.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J. Mach. Learn. Res. 13, 27–66 (2012)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Nakamura, R.Y.M., Pereira, L.A.M., Costa, K.A., Rodrigues, D., Papa, J.P.: BBA: A binary bat algorithm for feature selection. Images (SIBGRAPI) (2012)Google Scholar
  17. 17.
    Behjat, A.R., Mustapha, A., Nezamabadi–pour, H., Sulaiman, M.N., Mustapha, N.: Feature subset selection using binary gravitational search algorithm for intrusion detection system. Intell. Inf. Database Syst. 7803 (2013)Google Scholar
  18. 18.
    Lin, S.W., Lee, Z.J., Chen, S.C., Tseng, T.Y.,.: Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl. Soft Comput. 8(4) (2008)CrossRefGoogle Scholar
  19. 19.
    Zabidi, A., Khuan, L.Y., Mansor, W., Yassin, I.M., Sahak, R.: Binary particle swarm optimization for feature selection in detection of infants with hypothyroidism. In: International Conference of the IEEE Engineering in Medecine and Biology Socienty (EMBC) (2011)Google Scholar
  20. 20.
    Mirjalili, S., Mirjalili, S.M.: Lewis, A. Grey wolf optimizer. Adv. Eng. Softw. 69(1) (2014)CrossRefGoogle Scholar
  21. 21.
    Crawford, B., Soto, R., Astorga, G., Garcia, J., Castro, C., Paredes, F.: Putting continuous metaheuristics to work in binary search spaces. Complexity 17, 1–19 (2017)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Medjahed, S.A., Saadi, T.A., Benyettou, A., Ouali, M.: Binary Cuckoo search algorithm for band selection in hyperspectral image classification. IAENG Int. J. Comput. Sci. 42(3), 183–191 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre Universitaire Ahmed ZabanaRelizaneAlgérie
  2. 2.Thales Canada Inc.North YorkCanada
  3. 3.Computer Science DepartmentUniversity of SherbrookeSherbrookeCanada

Personalised recommendations