Vol.73, No.2, 2022, pp.3721-3739, doi:10.32604/cmc.2022.029619
A Sea Ice Recognition Algorithm in Bohai Based on Random Forest
  • Tao Li1, Di Wu1, Rui Han2, Jinyue Xia3, Yongjun Ren4,*
1 School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Unit 93117 of PLA, PLA, Jiangsu, 210000, China
3 International Business Machines Corporation (IBM), NY, 100014, USA
4 School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Yongjun Ren. Email:
Received 08 March 2022; Accepted 07 May 2022; Issue published 16 June 2022
As an important maritime hub, Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter, which greatly affects the socio-economic and development of the region. Therefore, this paper uses FY-4A (a weather satellite) data to study sea ice in the Bohai Sea. After processing the data for land removal and cloud detection, it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions. The random forests classification algorithm is introduced in sea ice identification, which can achieve a certain effect of sea ice classification recognition under cloud cover. Under non-clear sky conditions, the results of Bohai Sea ice identification based on random forests have been improved, and the algorithm can effectively identify Bohai Sea Ice and can improve the accuracy of sea ice identification, which lays a foundation for the accuracy and stability of sea ice identification. It realizes sea ice identification in the Bohai Sea and provides data support and algorithm support for marine climate forecasting related departments.
FY-4A; random forests; Bohai Sea Ice; sea ice identification; adaptive threshold
Cite This Article
T. Li, D. Wu, R. Han, J. Xia and Y. Ren, "A sea ice recognition algorithm in bohai based on random forest," Computers, Materials & Continua, vol. 73, no.2, pp. 3721–3739, 2022.
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