Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm
- 146 Downloads
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.
KeywordsSocial recognition Swarm intelligence Multi-objective optimization (MOO) Gravitational search algorithm (GSA) Remote sensing image classification
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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
- 14.Miettinen, K. Nonlinear multiobjective optimization, Springer Science & Business Media; 2012.Google Scholar
- 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.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.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
- 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
- 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.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
- 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.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
- 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.Yin B, Guo Z, Liang Z, et al. Improved gravitational search algorithm with crossover. Comput Electr Eng. 2017.Google Scholar