Skip to main content
SpringerLink
Log in

Multilevel Quantum Evolutionary Butterfly Optimization Algorithm for Automatic Clustering of Hyperspectral Images

  • Conference paper
  • First Online:
The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023 (AICV 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 164))

Abstract

Hyperspectral images contain a huge amount of spectral and spatial information. Automatic clustering of such images is a difficult task. Two algorithms viz., Qubit Evolutionary Butterfly Optimization algorithm and Qutrit Evolutionary Butterfly Optimization algorithm, are proposed for the automatic clustering of hyperspectral images. Implementing quantum mutation operators enhances the exploration phase and prevents it from getting stuck in local optima, a drawback of their classical version. This is the first time quantum versions of the Butterfly Optimization Algorithm are proposed and implemented on such a large dataset. The two proposed algorithms are compared to the classical Butterfly Optimization Algorithm and classical Evolutionary Butterfly Optimization Algorithms, and applied to the Xuzhou HYSPEX dataset. Statistical tests like mean, standard deviation, One-Way ANOVA test, and Tukey’s Post Hoc test are performed on all the algorithms to examine their efficiency. The Correlation Based Cluster Validity Index is used as the fitness function. The quality of clustering is determined using the F, F’, and Q scores. The qutrit version is found to produce more optimal results, followed by the qubit version. The qutrit version converges faster with better optimal results by using a smaller population compared to the other three algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23(3), 715–734 (2019)

    Article  Google Scholar 

  2. Bezdek, J.C., Ehrlich, R., Full, W.: Fcm: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)

    Article  Google Scholar 

  3. Bhattacharyya, S., Dutta, T., Dey, S.: Multilevel quantum inspired fractional order ant colony optimization for automatic clustering of hyperspectral images. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)

    Google Scholar 

  4. Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recogn. Lett. 19(8), 741–747 (1998)

    Article  MATH  Google Scholar 

  5. Cai, Y., Liu, X., Cai, Z.: Bs-nets: An end-to-end framework for band selection of hyperspectral image. IEEE Trans. Geosci. Remote Sens. 58(3), 1969–1984 (2020)

    Article  Google Scholar 

  6. Ding, C., et al.: Hyperspectral image classification promotion using clustering inspired active learninghyperspectral image classification promotion using clustering inspired active learning. Remote Sens. 14 (2022)

    Google Scholar 

  7. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)

    Google Scholar 

  8. Dutta, T., Bhattacharyya, S., Mukhopadhyay, S.: Automatic clustering of hyperspectral images using qutrit exponential decomposition particle swarm optimization. In: 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), pp. 289–292 (2021)

    Google Scholar 

  9. Ghosh, Swarup Kr, Ghosh, Anupam: Correlation based cluster validity index for recognition of leukemia mediating biomarkers. In: Mandal, Jyotsna Kumar, De, Debashis (eds.) EAIT 2021. LNNS, vol. 292, pp. 65–74. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4435-1_8

    Chapter  Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  11. Lei, J., Li, X., Peng, B., Fang, L., Ling, N., Huang, Q.: Deep spatial-spectral subspace clustering for hyperspectral image. IEEE Trans. Circ. Syst. Video Technol. 31(7), 2686–2697 (2021)

    Article  Google Scholar 

  12. Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1650–1654 (2002)

    Google Scholar 

  13. Rödel, E., Fisher, R.A.: Statistical methods for research workers, 14. aufl., oliver & boyd, Edinburgh, London 1970. xiii, 362 s., 12 abb., 74 tab., 40 s. Biometrische Zeitschrift 13(6), 429–430 (1971)

    Google Scholar 

  14. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MATH  Google Scholar 

  15. Tan, K., Wu, F., Du, Q., Du, P., Chen, Y.: A parallel gaussian-bernoulli restricted boltzmann machine for mining area classification with hyperspectral imagery. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 12(2), 627–636 (2019)

    Article  Google Scholar 

  16. Tkachuk, V.: Quantum genetic algorithm based on qutrits and its application. Math. Prob. Eng. 2018(8614073) (2018)

    Google Scholar 

  17. Tubishat, M., Alswaitti, M., Mirjalili, S., Al-Garadi, M.A., Alrashdan, M.T., Rana, T.A.: Dynamic butterfly optimization algorithm for feature selection. IEEE Access 8, 194303–194314 (2020). https://doi.org/10.1109/ACCESS.2020.3033757

    Article  Google Scholar 

  18. Tukey, J.W., et al.: Exploratory data analysis, vol. 2. Reading, MA (1977)

    Google Scholar 

  19. Wang, X., Tan, K., Du, Q., Chen, Y., Du, P.: Caps-triplegan: gan-assisted capsnet for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(9), 7232–7245 (2019)

    Article  Google Scholar 

  20. Weijtmans, P., Shan, C., Tan, T., de Koning, S., Ruers, T.J.M.: A dual stream network for tumor detection in hyperspectral images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1256–1259 (2019)

    Google Scholar 

  21. Zhao, J., Yan, H., Huang, L.: A joint method of spatial-spectral features and bp neural network for hyperspectral image classification. Egypt. J. Remote Sens. Space Sci. 26(1), 107–115 (2023)

    Google Scholar 

  22. Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: a 3-d deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847–858 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Presidency University, Bangalore, India

    Tulika Dutta

  2. Rajnagar Mahavidyalaya, Rajnagar, Birbhum, India

    Siddhartha Bhattacharyya

  3. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Siddhartha Bhattacharyya & Bijaya Ketan Panigrahi

Authors
  1. Tulika Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siddhartha Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bijaya Ketan Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tulika Dutta .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  4. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    KC Santosh

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  6. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Słowik

  7. Department of Computer Science, Avinashilingam University for Women, Coimbatore, Tamil Nadu, India

    Parthasarathy Subashini

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dutta, T., Bhattacharyya, S., Panigrahi, B.K. (2023). Multilevel Quantum Evolutionary Butterfly Optimization Algorithm for Automatic Clustering of Hyperspectral Images. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_48

Download citation

Publish with us

Policies and ethics

Search

Navigation