TY - JOUR
T1 - A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data
AU - Zhou, Xiran
AU - Li, WenWen
AU - Arundel, Samantha T.
N1 - Funding Information:
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.
Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/4/3
Y1 - 2019/4/3
N2 - 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.
AB - 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.
KW - Multi-neighborhood analysis
KW - lidar point cloud
KW - multi-scale data fusion
KW - probabilistic modeling
KW - terrain feature extraction
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U2 - 10.1080/13658816.2018.1554814
DO - 10.1080/13658816.2018.1554814
M3 - Article
AN - SCOPUS:85059088406
SN - 1365-8816
VL - 33
SP - 666
EP - 686
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
IS - 4
ER -