Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-Temporal Clustering

  • Asanobu Kitamoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2534)


The purpose of this paper is to analyze the evolution of typhoon cloud patterns in the spatio-temporal domain using statistical learning models. The basic approach is clustering procedures for extracting hidden states of the typhoon, and we also analyze the temporal dynamics of the typhoon in terms of transitions between hidden states. The clustering procedures include both spatial and spatio-temporal clustering procedures, including K-means clustering, Self-Organizing Maps (SOM), Mixture of Gaussians (MoG) and Generative Topographic Mapping (GTM) combined with Hidden Markov Model (HMM). The result of clustering is visualized on the ”Evolution Map” on which we analyze and visualize the temporal structure of the typhoon cloud patterns. The results show that spatio-temporal clustering procedures outperform spatial clustering procedures in capturing the temporal structures of the evolution of the typhoon.


Hide Markov Model Hide State Cluster Procedure Modeling State Transition Generative Topographic Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Asanobu Kitamoto
    • 1
  1. 1.National Institute of InformaticsTokyoJapan

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