A Hidden Markov Contour Tree Model for Spatial Structured Prediction
Spatial structured models are predictive models that capture dependency structure between samples based on their locations in the space. Learning such models plays an important role in many geoscience applications, but it also poses significant challenges due to implicit dependency structure in continuous space and high computational costs. Existing models often assume that the dependency structure is based on either spatial proximity or network topology, and thus cannot incorporate complex dependency structure such as contour and flow direction on a 3D potential surface. To fill the gap, we recently proposed hidden Markov contour tree (HMCT), which generalizes the traditional hidden Markov model from a total order sequence to a partial order polytree. HMCT also advances existing work on hidden Markov trees through capturing complex contour structures. We proposed efficient model construction and learning algorithms. This paper extends our initial HMCT model into a post-processor. We analyzed the theoretical properties of the extended model. Evaluations on real-world flood mapping datasets show that HMCT outperforms multiple baseline methods in classification performance and the HMCT post-processor can also effectively enhance other baseline methods. Computational experiments show that HMCT is scalable to large data sizes (e.g., classifying millions of samples in seconds).
Branch: CSE Domain: Data Mining