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.
Abstract:
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.
