Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)

TL;DR

This paper introduces a geographical hidden Markov tree model that improves flood extent mapping by capturing complex spatial dependencies, outperforming traditional methods on synthetic and real datasets.

Contribution

It proposes a novel probabilistic graphical model extending HMMs to 2D maps with anisotropic spatial dependency, along with scalable algorithms for its implementation.

Findings

Outperforms baseline flood mapping methods

Effective on both synthetic and real datasets

Scalable to large data sizes

Abstract

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. Anisotropic spatial 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.