A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data
International Journal of Geographical Information Science
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
Abstract
This paper introduces our research in developing a probabilistic model to extract linear terrain features from high resolution DEM (Digital Elevation Model) data. 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 feature, 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, and to support 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, object-based image analysis and edge detection. We hope this work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.
Suggested Citation
Zhou, X., Li, W., and Arundel, S., 2019, A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data: International Journal of Geographical Information Science, v. 33, no. 4, p. 666-686, https://doi.org/10.1080/13658816.2018.1554814.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data |
| Series title | International Journal of Geographical Information Science |
| DOI | 10.1080/13658816.2018.1554814 |
| Volume | 33 |
| Issue | 4 |
| Publication Date | December 21, 2018 |
| Year Published | 2019 |
| Language | English |
| Publisher | Taylor and Francis |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 21 p. |
| First page | 666 |
| Last page | 686 |