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Cognitive Computation

, Volume 11, Issue 6, pp 789–798 | Cite as

Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm

  • Aizhu Zhang
  • Sihan Liu
  • Genyun SunEmail author
  • Hui Huang
  • Ping Ma
  • Jun Rong
  • Hongzhang Ma
  • Chengyan Lin
  • Zhenjie Wang
Article

Abstract

Cognitively inspired swarm intelligence algorithms (SIAs) have attracted much attention in the research area of clustering since it can give machine the ability of self-learning to achieve better classification results. Recently, the SIA-based multi-objective optimization (MOO) methods have shown their superiorities in data clustering. However, their performances are limited when applying to the clustering of remote sensing imagery (RSI). To construct an excellent MOO-based clustering method, this paper presents a social recognition-based multi-objective gravitational search algorithm (SMGSA) to achieve simultaneous optimization of two conflicting cluster validity indices, i.e., the Xie-Beni (XB) index and the Jm index. In the SMGSA, searching particles not only are guided by those elite particles stored in an external archive by the gravitational force but also learn from the social recognition of the whole population through the position difference. SMGSA thereby formed with outstanding exploitation ability. Comparison experiments on two public RSI data sets, including a moderate aerial image and a hyperspectral, validated that the MOO-based clustering methods could obtain more accurate results than the single validity index-based method. Moreover, the SMGSA-based method can achieve superior results than that of the multi-objective gravitational search algorithm without social recognition ability. The proposed SMGSA performs favorable balance between the two conflicting cluster validity indices and achieves preferable classification for the RSI. In addition, this study indicates that the swarm intelligence-based cognitive computing is potential for the intelligent interpretation and understanding of complicated remote sensing scene.

Keywords

Social recognition Swarm intelligence Multi-objective optimization (MOO) Gravitational search algorithm (GSA) Remote sensing image classification 

Notes

Funding

This study was funded by the National Natural Science Foundation of China (41471353), the Natural Science Foundation of Shandong Province (ZR201709180096, ZR201702100118), the Fundamental Research Funds for the Central Universities (18CX05030A, 18CX02179A), and the Postdoctoral Application and Research Projects of Qingdao (BY20170204).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Huang XX, Huang HX, Liao BS, et al. An ontology-based approach to metaphor cognitive computation. Mind Mach. 2013;23(1):105–21.CrossRefGoogle Scholar
  2. 2.
    Ding S, Zhang J, Jia H, et al. An adaptive density data stream clustering algorithm. Cogn Comput. 2016;8(1):30–8.CrossRefGoogle Scholar
  3. 3.
    Kim SS, McLoone S, Byeon JH, et al. Cognitively inspired artificial bee colony clustering for cognitive wireless sensor networks. Cogn Comput. 2017;9(2):207–24.CrossRefGoogle Scholar
  4. 4.
    Siddique N, Adeli H. Nature-inspired chemical reaction optimisation algorithms. Cogn Comput. 2017;9(4):411–22.CrossRefGoogle Scholar
  5. 5.
    Nanda SJ, Panda G. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput. 2014;16:1–18.CrossRefGoogle Scholar
  6. 6.
    Chakraborty S, Dey N, Samanta S, et al. Optimization of non-rigid demons registration using cuckoo search algorithm. Cogn Comput. 2017;9(6):817–26.CrossRefGoogle Scholar
  7. 7.
    Tang Q, Shen Y, Hu C, et al. Swarm intelligence: based cooperation optimization of multi-modal functions. Cogn Comput. 2013;5(1):48–55.CrossRefGoogle Scholar
  8. 8.
    Mukhopadhyay A, Bandyopadhyay S, Maulik U. Clustering using multi-objective genetic algorithm and its application to image segmentation[C]//Systems, Man and Cybernetics, 2006. SMC'06 IEEE International Conference on IEEE. 2006;3:2678–2683.Google Scholar
  9. 9.
    Bong CW, Rajeswari M. Multi-objective nature-inspired clustering and classification techniques for image segmentation. Appl Soft Comput. 2011;11:3271–82.CrossRefGoogle Scholar
  10. 10.
    Ma A, Zhong Y, Zhang L. Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery. IEEE Trans Geosci Remote Sens. 2015;53(8):4202–17.CrossRefGoogle Scholar
  11. 11.
    Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput. 1994;2(3):221–48.CrossRefGoogle Scholar
  12. 12.
    Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput. 2004;8:256–79.CrossRefGoogle Scholar
  13. 13.
    Mousa AA, El-Shorbagy MA, Abd-El-Wahed WF. Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm Evol Comput. 2012;3:1–14.CrossRefGoogle Scholar
  14. 14.
    Miettinen, K. Nonlinear multiobjective optimization, Springer Science & Business Media; 2012.Google Scholar
  15. 15.
    Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput. 2002;6(2):182–97.CrossRefGoogle Scholar
  16. 16.
    Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput. 2014;8(2):173–95.CrossRefGoogle Scholar
  17. 17.
    Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis DT, Périaux J, Papailiou KD, Fogarty T, editors. Evolutionary methods for design, optimization and control with applications to industrial problems. Berlin: Springer-Verlag; 2002. p. 95–100.Google Scholar
  18. 18.
    Zitzler E, Künzli S. Indicator-based selection in multiobjective search[C]//International Conference on Parallel Problem Solving from Nature. Springer, Berlin, Heidelberg; 2004:832–842.Google Scholar
  19. 19.
    Phan DH, Suzuki J. R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization[C]//Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE; 2013:1836–1845.Google Scholar
  20. 20.
    Zhang Q, Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Trans Evol Comput. 2007;11(6):712–31.CrossRefGoogle Scholar
  21. 21.
    Liu H L, Gu F, Cheung Y. T-MOEA/D: MOEA/D with objective transform in multi-objective problems[C]//Information Science and Management Engineering (ISME), 2010 International Conference of. IEEE; 2010;2:282–285.Google Scholar
  22. 22.
    Bandyopadhyay S, Maulik U, Mukhopadhyay A. Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Trans Geosci Remote Sens. 2007;45:1506–11.CrossRefGoogle Scholar
  23. 23.
    Mukhopadhyay A, Maulik U. Unsupervised pixel classification in satellite imagery using multiobjective fuzzy clustering combined with SVM classifier. IEEE Trans Geosci Remote Sens. 2009;47(4):1132–8.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. 2009;47(12):4175–88.CrossRefGoogle Scholar
  25. 25.
    Zhang M, Jiao L, Ma W, et al. Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D. Appl Soft Comput. 2016;48:621–37.CrossRefGoogle Scholar
  26. 26.
    Zhong Y, Zhang S, Zhang L. Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery. IEEE J-STARS. 2013;6(5):2290–301.Google Scholar
  27. 27.
    Zhong Y, Ma A, Zhang L. An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery. IEEE J-STARS. 2014;7(4):1235–48.Google Scholar
  28. 28.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inform Sciences. 2009;179(13):2232–48.CrossRefGoogle Scholar
  29. 29.
    Han X, Chang X, Quan L, et al. Feature subset selection by gravitational search algorithm optimization. Inf Sci. 2014;281:128–46.CrossRefGoogle Scholar
  30. 30.
    Mirjalili S, Lewis A. Adaptive gbest-guided gravitational search algorithm. Neural Comput & Applic. 2014;25(7–8):1569–84.CrossRefGoogle Scholar
  31. 31.
    Zhang A, Sun G, Wang Z, et al. A hybrid genetic algorithm and gravitational search algorithm for global optimization. Neural Netw World. 2015;25(1):53–73.CrossRefGoogle Scholar
  32. 32.
    Zhang A, Sun G, Ren J, et al. A dynamic neighborhood learning-based gravitational search algorithm. IEEE Transactions on Cybernetics. 2018;48(1):436–47.PubMedCrossRefGoogle Scholar
  33. 33.
    Hassanzadeh H R, Rouhani M. A multi-objective gravitational search algorithm[C]//Computational Intelligence, Communication Systems and Networks (CICSyN), 2010 Second International Conference on. IEEE Int Conf Comput Intell Commun Syst (CICSyN); 2010:7–12.Google Scholar
  34. 34.
    Nobahari H, Nikusokhan M, Siarry P. Non-dominated sorting gravitational search algorithm[C]//Proc. of the 2011 International Conference on Swarm Intelligence, ICSI; 2011:1–10.Google Scholar
  35. 35.
    Nobahari H, Nikusokhan M, Siarry P. A multi-objective gravitational search algorithm based on non-dominated sorting[J]. International Journal of Swarm Intelligence Research (IJSIR). 2012;3(3):32–49.CrossRefGoogle Scholar
  36. 36.
    Sun G, Zhang A, Jia X, et al. DMMOGSA: diversity-enhanced and memory-based multi-objective gravitational search algorithm. Inform Sciences. 2016;363:52–71.CrossRefGoogle Scholar
  37. 37.
    Zhang A, Sun G, Wang Z. Remote sensing imagery classification using multi-objective gravitational search algorithm[C]//Image and Signal Processing for Remote Sensing XXII. International Society for Optics and Photonics. 2016;10004:100041I.Google Scholar
  38. 38.
    Yin B, Guo Z, Liang Z, et al. Improved gravitational search algorithm with crossover. Comput Electr Eng. 2017.Google Scholar
  39. 39.
    Xie XL, Beni G. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell. 1991;13(8):841–7.CrossRefGoogle Scholar
  40. 40.
    Bezdek JC. Pattern recognition with fuzzy objective function algorithms. USA: Plenum Press; 1981.CrossRefGoogle Scholar
  41. 41.
    Guo W, Wang L, Wu Q. Numerical comparisons of migration models for multi-objective biogeography-based optimization. Inf Sci. 2016;328:302–20.CrossRefGoogle Scholar
  42. 42.
    Mirjalili S, Saremi S, Mirjalili SM, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl. 2016;47:106–19.CrossRefGoogle Scholar
  43. 43.
    Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell. 2002;24(12):1650–4.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of GeosciencesChina University of Petroleum (East China)QingdaoChina
  2. 2.Laboratory for Marine Mineral ResourcesQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.Satellite Environment CenterMinistry of Environmental Protection of ChinaBeijingChina
  4. 4.College of ScienceChina University of Petroleum (East China)QingdaoChina

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