Geographical Hidden Markov Tree for Flood Extent Mapping
Geographical Hidden Markov Tree for Flood Extent Mapping
复制标题
DOI：
10.1145/3219819.3220053
复制
发表时间：
2018-05
期刊：
影响因子：
--
通讯作者：
Miao Xie;Zhe Jiang;Arpan Man Sainju
中科院分区：
文献类型：
--
作者：
Miao Xie;Zhe Jiang;Arpan Man Sainju
洪水范围图在灾害管理和国家水资源预报中起着至关重要的作用。遗憾的是，传统的分类方法往往受到噪声、障碍物和光谱特征的异质性以及类别标签之间隐含的各向异性空间相关性的影响。在本文中，我们提出了地理隐马尔可夫树，这是一种概率图模型，它将常见的隐马尔可夫模型从一维序列推广到二维映射。偏序类依赖被引入到具有倒排树结构的隐藏类层中。我们还研究了反向树构造、模型参数学习和类推理的计算算法。在合成数据集和真实数据集上的广泛评估表明，所提出的模型在洪水映射方面优于多基线，并且我们的算法在大数据量上是可伸缩的。
Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In this paper, we propose geographical hidden Markov tree, a probabilistic graphical model that generalizes the common hidden Markov model from a one dimensional sequence to a two dimensional map. Partial order class dependency is incorporated in the hidden class layer with a reverse tree structure. We also investigate computational algorithms for reverse tree construction, model parameter learning and class inference. Extensive evaluations on both synthetic and real world datasets show that proposed model outperforms multiple baselines in flood mapping, and our algorithms are scalable on large data sizes.