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Knowledge mapping and trends in research on remote sensing change detection using CiteSpace analysis

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Abstract

Detection of change through remote sensing (RS) is widely used in Earth observation and environment surveys, whereas the introduction of bibliometric methods to the development, application, and identification of trends in RS change detection (RSCD) remain limited. Based on the 5,012 published academic studies in the Web of Science Core Collection (WOSCC) database between 2000 and 2022, and CiteSpace software, the publications built the RSCD knowledge mapping about cooperation network, literature co-citation, keyword co-occurrence, and burst detection analyses. The result shown that: (1) There has been a significant increasing trend in RSCD-related literature over the last two decades. Among these, Remote Sensing and IEEE journals had the highest number of publications. (2) China, the United States, and Italy, are the top three in the number of publications, mainly by various universities and research institutes. Bruzzone Lorenzo, Gong Maoguo, Bovolo Francesca, and other authors have made important contributions. (3) Among highly cited literature, the “changed object” was the first focus of CD research, and 17 research clusters were identified, including semantic, terrain correction, land cover, synthetic aperture radar, and the unsupervised. (4) Main research topics included CD models, unsupervised CD algorithms, and land cover classification. Research hots included deep learning, misregistration, image segmentation, and Google Earth Engine. This study provided the multidimensional references for researchers, practitioners, and institutions in the current trends, topics, and hots of RSCD.

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Availability of data and materials

The datasets generated and/or analysed during the current study are available from the Web of Science Core Collection (WOSCC) database. Supplementary material to this article can be found at appendix A and B.

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Acknowledgements

The Author would like to thank the anonymous reviewers for their valuable comments and suggestions for revising and improving the article.

Funding

This work was supported by National Natural Science Foundation of China, grant number: 41471283; Postgraduate Research & Practice Innovation Program of Jiangsu Province, grant number: KYCX22_1577; and Nanjing Normal University Doctoral Dissertation Excellent Topic Funding Program, grant number: YXXT21-042.

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Yuanhe Yu collected and analyzed data, and wrote original manuscript. Yuzhen Shen, Yaoyao Liu, Xudong Rui and Bingbing Li reviewed the manuscript. Yuchun Wei reviewed the manuscript, and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yuchun Wei.

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Communicated by: H. Babaie

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Yu, Y., Shen, Y., Liu, Y. et al. Knowledge mapping and trends in research on remote sensing change detection using CiteSpace analysis. Earth Sci Inform 16, 787–801 (2023). https://doi.org/10.1007/s12145-022-00914-4

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