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.
Similar content being viewed by others
References
Huang XX, Huang HX, Liao BS, et al. An ontology-based approach to metaphor cognitive computation. Mind Mach. 2013;23(1):105–21.
Ding S, Zhang J, Jia H, et al. An adaptive density data stream clustering algorithm. Cogn Comput. 2016;8(1):30–8.
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.
Siddique N, Adeli H. Nature-inspired chemical reaction optimisation algorithms. Cogn Comput. 2017;9(4):411–22.
Nanda SJ, Panda G. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput. 2014;16:1–18.
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.
Tang Q, Shen Y, Hu C, et al. Swarm intelligence: based cooperation optimization of multi-modal functions. Cogn Comput. 2013;5(1):48–55.
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.
Bong CW, Rajeswari M. Multi-objective nature-inspired clustering and classification techniques for image segmentation. Appl Soft Comput. 2011;11:3271–82.
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.
Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput. 1994;2(3):221–48.
Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput. 2004;8:256–79.
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.
Miettinen, K. Nonlinear multiobjective optimization, Springer Science & Business Media; 2012.
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.
Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput. 2014;8(2):173–95.
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.
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.
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.
Zhang Q, Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Trans Evol Comput. 2007;11(6):712–31.
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.
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.
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.
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.
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.
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.
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.
Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inform Sciences. 2009;179(13):2232–48.
Han X, Chang X, Quan L, et al. Feature subset selection by gravitational search algorithm optimization. Inf Sci. 2014;281:128–46.
Mirjalili S, Lewis A. Adaptive gbest-guided gravitational search algorithm. Neural Comput & Applic. 2014;25(7–8):1569–84.
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.
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.
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.
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.
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.
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.
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.
Yin B, Guo Z, Liang Z, et al. Improved gravitational search algorithm with crossover. Comput Electr Eng. 2017.
Xie XL, Beni G. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell. 1991;13(8):841–7.
Bezdek JC. Pattern recognition with fuzzy objective function algorithms. USA: Plenum Press; 1981.
Guo W, Wang L, Wu Q. Numerical comparisons of migration models for multi-objective biogeography-based optimization. Inf Sci. 2016;328:302–20.
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.
Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell. 2002;24(12):1650–4.
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Rights and permissions
About this article
Cite this article
Zhang, A., Liu, S., Sun, G. et al. Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm. Cogn Comput 11, 789–798 (2019). https://doi.org/10.1007/s12559-018-9582-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-018-9582-9