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


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.


Social 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.

Ethical Approval

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


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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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