A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data

Xiran Zhou, WenWen Li, Samantha T. Arundel

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This article introduces our research in developing a probabilistic model to extract linear terrain features from high resolution Digital Elevation Models (DEMs). The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear terrain features, such as ridgelines, valley lines and crater boundaries, and then adopts multiple neighborhood analysis and a probability model to address data uncertainty in terrain surface modeling. Different from traditional approaches, the proposed model has the ability to achieve near-automated processing. It also supports effective extraction of terrain features in both smooth and rough surfaces. Through a series of experiments, we demonstrate that the proposed approach outperforms existing techniques, including thresholding, stream/drainage network analysis, visual descriptor detection, object-based image analysis and edge detection. This work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.

Original languageEnglish (US)
Pages (from-to)666-686
Number of pages21
JournalInternational Journal of Geographical Information Science
Volume33
Issue number4
DOIs
StatePublished - Apr 3 2019

Keywords

  • Multi-neighborhood analysis
  • lidar point cloud
  • multi-scale data fusion
  • probabilistic modeling
  • terrain feature extraction

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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