Advertisement

Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey

  • Yuting Wan
  • Yanfei Zhong
  • Ailong MaEmail author
  • Liangpei Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

This paper presents a survey of evolutionary computation theory for remote sensing image clustering. With the ongoing development of Earth observation techniques, remote sensing data has entered the era of big data, so it is difficult for researchers to get more prior knowledge. In recent years, many experts and scholars have a strong interest in remote sensing clustering due to it does not require training samples. However, remote sensing image clustering has always been a challenging task because of the inherent complexity of remote sensing images, the huge amount of data and so on. Normally, the clustering problem of remote sensing images is transformed into the optimization problem of fuzzy clustering objective function, the goal of which lies in the identification of correct cluster centers in the eigenspace. But traditional clustering approaches belong to hill climbing methods, which are greatly affected by initial values and easily get stuck in local optima. Evolutionary computation techniques are inspired by biological evolution, which can provide possible solutions to find the better clustering centers. So, researchers have carried out a series of related studies. Here, we provide an overview, including: (1) evolutionary single-objective; (2) evolutionary multi-objective; (3) memetic algorithm.

Keywords

Remote sensing clustering Evolutionary computation Evolutionary algorithms 

References

  1. 1.
    Jain, A.K.: Data clustering: 50 years beyond k-means. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS, vol. 5211, pp. 3–4. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-87479-9_3 CrossRefGoogle Scholar
  2. 2.
    Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A.: A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C 39(2), 133–155 (2009)CrossRefGoogle Scholar
  3. 3.
    Zhang, M., Ma, J., Gong, M., Li, H., Liu, J.: Memetic algorithm based feature selection for hyperspectral images classification. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 495–502. IEEE (2017)Google Scholar
  4. 4.
    Yu, H., Jiao, L., Liu, F.: CRIM-FCHO: SAR image two-stage segmentation with multifeature ensemble. IEEE Trans. Geosci. Remote Sens. 54(4), 2400–2423 (2016)CrossRefGoogle Scholar
  5. 5.
    Jiao, L., Tang, X., Hou, B., Wang, S.: SAR images retrieval based on semantic classification and region-based similarity measure for earth observation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(8), 3876–3891 (2015)CrossRefGoogle Scholar
  6. 6.
    Ghosh, A., Mishra, N.S., Ghosh, S.: Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf. Sci. 181(4), 699–715 (2011)CrossRefGoogle Scholar
  7. 7.
    Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recogn. Lett. 17(8), 825–832 (1996)CrossRefGoogle Scholar
  8. 8.
    Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(1), 218–237 (2008)CrossRefGoogle Scholar
  9. 9.
    Zhang, S., Zhong, Y., Zhang, L.: An automatic fuzzy clustering algorithm based on self-adaptive differential evolution for remote sensing image. Acta Geodaetica Cartogr. Sin. 42(2), 239–246 (2013)Google Scholar
  10. 10.
    Zhong, Y., Zhang, L.: A new fuzzy clustering algorithm based on clonal selection for land cover classification. Math. Probl. Eng. 2011(2), 253–266 (2011)Google Scholar
  11. 11.
    Bandyopadhyay, S.: Satellite image classification using genetically guided fuzzy clustering with spatial information. Int. J. Remote Sens. 26(3), 579–593 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bandyopadhyay, S.: Genetic algorithms for clustering and fuzzy clustering. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(6), 524–531 (2011)CrossRefGoogle Scholar
  13. 13.
    Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets Syst. 155(2), 191–214 (2005)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. 28(C), 69–80 (2015)CrossRefGoogle Scholar
  15. 15.
    Ma, A., Zhong, Y., Zhang, L.: Adaptive differential evolution fuzzy clustering algorithm with spatial information and kernel metric for remote sensing imagery. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 278–285. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41278-3_34 CrossRefGoogle Scholar
  16. 16.
    Zhong, Y., Ma, A., Zhang, L.: An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7(4), 1235–1248 (2014)CrossRefGoogle Scholar
  17. 17.
    Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Deb, K.: Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem. J. Glob. Optim. 41(4), 479–515 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 742–756. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-70928-2_56 CrossRefGoogle Scholar
  20. 20.
    Bandyopadhyay, S., Maulik, U., Mukhopadhyay, A.: Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 45(5), 1506–1511 (2007)CrossRefGoogle Scholar
  21. 21.
    Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comp. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  22. 22.
    Mukhopadhyay, A., Maulik, U.: Unsupervised pixel classification in satellite imagery using multiobjective fuzzy clustering combined with SVM classifier. IEEE Trans. Geosci. Remote Sens. 47(4), 1132–1138 (2009)CrossRefGoogle Scholar
  23. 23.
    Kaushik, S., Debarati, K., Sayan, G., Swagatam, D., Ajith, A., Han, S.Y.: Multi-objective differential evolution for automatic clustering with application to micro-array data analysis. Sensors 9(5), 3981–4004 (2009)CrossRefGoogle Scholar
  24. 24.
    Paoli, A., Melgani, F., Pasolli, E.: Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 47(12), 4175–4188 (2009)CrossRefGoogle Scholar
  25. 25.
    Li, Y., Feng, S., Zhang, X., Jiao, L.: SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm. Inf. Process. Lett. 114(6), 287–293 (2014)CrossRefzbMATHGoogle Scholar
  26. 26.
    Naeini, A.A., Homayouni, S., Saadatseresht, M.: Improving the dynamic clustering of hyperspectral data based on the integration of swarm optimization and decision analysis. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7(6), 2161–2173 (2014)CrossRefGoogle Scholar
  27. 27.
    Zhong, Y., Zhang, S., Zhang, L.: Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6(5), 2290–2301 (2013)CrossRefGoogle Scholar
  28. 28.
    Luo, J., Jiao, L., Lozano, J.A.: A sparse spectral clustering framework via multiobjective evolutionary algorithm. IEEE Trans. Evol. Comput. 20(3), 418–433 (2016)CrossRefGoogle Scholar
  29. 29.
    Li, L., Yao, X., Stolkin, R., Gong, M., He, S.: An evolutionary multiobjective approach to sparse reconstruction. IEEE Trans. Evol. Comput. 18(6), 827–845 (2014)CrossRefGoogle Scholar
  30. 30.
    Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. 45(3), 1–33 (2013)zbMATHGoogle Scholar
  31. 31.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program. C3P Report 826 (1989)Google Scholar
  32. 32.
    Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)CrossRefGoogle Scholar
  33. 33.
    Zhu, Z., Jia, S., Ji, Z.: Towards a memetic feature selection paradigm. IEEE Comput. Intell. Mag. 5(2), 41–53 (2010)CrossRefGoogle Scholar
  34. 34.
    Chen, X., Feng, L., Soon Ong, Y.: A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem. Int. J. Syst. Sci. 43(7), 1347–1366 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Özcan, E., Başaran, C.: A case study of memetic algorithms for constraint optimization. Soft. Comput. 13(8), 871 (2009)CrossRefGoogle Scholar
  36. 36.
    Jiao, L., Gong, M., Wang, S., Hou, B.: Natural and remote sensing image segmentation using memetic computing. IEEE Comput. Intell. Mag. 5(2), 78–91 (2010)CrossRefGoogle Scholar
  37. 37.
    Ma, A., Zhong, Y., Zhang, L.: Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 53(8), 4202–4217 (2015)CrossRefGoogle Scholar
  38. 38.
    Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. 36(1), 141–152 (2006)CrossRefGoogle Scholar
  39. 39.
    Ong, Y.S., Lim, M.H., Chen, X.: Research frontier: memetic computation-past, present and future. IEEE Comput. Intell. Mag. 5(2), 24–31 (2010)CrossRefGoogle Scholar
  40. 40.
    Gong, M., Li, H., Jiang, X.: A multi-objective optimization framework for ill-posed inverse problems in image processing ☆. CAAI Trans. Intell. Technol. 1(3), 225–240 (2016)CrossRefGoogle Scholar
  41. 41.
    Kabanikhin, S.I.: Definitions and examples of inverse and ill-posed problems. J. Inverse Ill-posed Probl. 16(4), 317–357 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Hauschild, M., Pelikan, M.: An introduction and survey of estimation of distribution algorithms. Swarm Evol. Comput. 1(3), 111–128 (2011)CrossRefGoogle Scholar
  43. 43.
    Zhou, A., Zhang, Q., Jin, Y.: Approximating the set of pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Trans. Evol. Comput. 13(5), 1167–1189 (2009)CrossRefGoogle Scholar
  44. 44.
    Zhang, J., Zhou, A., Zhang, G.: A multiobjective evolutionary algorithm based on decomposition and preselection. In: Gong, M., Pan, L., Song, T., Tang, K., Zhang, X. (eds.) BIC-TA 2015. CCIS, vol. 562, pp. 631–642. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-49014-3_56 CrossRefGoogle Scholar
  45. 45.
    Lin, X., Zhang, Q., Kwong, S.: A decomposition based multiobjective evolutionary algorithm with classification. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Canada, pp. 3292–3299. IEEE (2016). doi: 10.1109/CEC.2016.7744206
  46. 46.
    Mussi, L., Daolio, F., Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the CUDA TM architecture. Inf. Sci. 181(20), 4642–4657 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuting Wan
    • 1
  • Yanfei Zhong
    • 1
  • Ailong Ma
    • 1
    Email author
  • Liangpei Zhang
    • 1
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhanChina

Personalised recommendations