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

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is funded in part by USGS grant #G15AC00085 and the CAREER program of the National Science Foundation NSF-BCS 1455349. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Notes on contributors

Xiran Zhou

Xiran Zhou is PhD candidate in the School of Geographical Sciences and Urban Planning at Arizona State University. His research interest is computer vision, photogrammetry and remote sensing and spatial data mining.

Wenwen Li

Wenwen Li is Associate Professor and Director of the Cyberinfrastructure and Computational Intelligence (CICI) Lab in the School of Geographical Sciences and Urban Planning at Arizona State University. Her research interest is cyberinfrastructure, space-time big data analytics and machine learning. She led the team who developed PolarHub - a large-scale web crawling engine for distributed geospatial data and PolarGlobe - a web-based scientific visualization tool for Earth science data.

Samantha T. Arundel

Samantha T. Arundel is a research geographer in the Center of Excellence for Geospatial Information Science at the U.S. Geological Survey. Her research currently focuses on automating natural feature mapping and modeling using various techniques like traditional raster modeling, GEOBIA and machine learning. She headed the development team in the automation of the 3D Elevation Program as it superseded the National Elevation Dataset.