基于隐马尔可夫轮廓树模型的空间结构预测

SPATIAL STRUCTURE PREDICTION BASED ON HIDDEN MARKOV CONTOUR TREE MODEL

  • 摘要: 为了实现地理信息中复杂依赖结构的合并,提出一种基于隐马尔可夫轮廓树模型的空间结构预测方法。将隐马尔可夫模型从全序序列推广到偏序多元序列,通过在曲面上捕捉复杂的轮廓结构,从而反映三维表面上所有位置之间的流动方向。另外,还提出基于等高线树节点折叠学习算法。进一步将该模型从生成型扩展到判别型,以便该模型可以用于后处理器。在真实洪涝地区数据集上进行了实验验证,结果表明了该方法的优越性。

     

    Abstract: In order to realize the merging of complex dependency structures, a spatial structure prediction method based on Hidden Markov contour tree model is proposed. The common hidden Markov model was extended from totally ordered sequence to partially ordered multivariate sequence. By capturing the complex contour structure on the surface, the flow direction between all positions on the three-dimensional surface was reflected. A node folding learning algorithm based on contour tree was proposed. An extension of the model was proposed, which was extended from generative type to discriminant type, so that the model could be used as post processor. Experiments were carried out on the real flood map data set. The results show the superiority of the proposed method.

     

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