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A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

Lili He1,2, Dantong Ouyang1,2, Meng Wang1,2, Hongtao Bai1,2, Qianlong Yang1,2, Yaqing Liu3,4, Yu Jiang1,2,*

College of Computer Science and Technology, Jilin University, Changchun, 130012, China .
Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, 130012, China.
School of Information Science & Technology, Dalian Maritime University, Dalian, 116026, China.
School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.

* Corresponding Author: Yu Jiang. Email: email.

Computers, Materials & Continua 2018, 57(3), 549-570.


In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters, thunderstorm cloud clusters are quickly and accurately identified. The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.


Cite This Article

L. He, D. Ouyang, M. Wang, H. Bai, Q. Yang et al., "A method of identifying thunderstorm clouds in satellite cloud image based on clustering," Computers, Materials & Continua, vol. 57, no.3, pp. 549–570, 2018.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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