Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs
Abstract
Sinkhole detection in karst areas is usually difficult through remote sensing image interpretation. We present an efficient approach to extract mature sinkholes from lidar DEM. First, an adaptive Wiener filter (AWF) and hierarchical watershed segmentation (HWs) are applied to identify all local depression or potential sinkholes. Second, a hole-filling algorithm is applied to the potential sinkholes, and nine spatial features are extracted. Finally, the random forest classifier is used to select true sinkholes from all potential sinkholes. Our results show that this approach is efficient for detecting mature sinkholes from lidar data, and it can be used for risk assessment and hazard preparedness in karst areas.
Department(s)
Geography, Geology, and Planning
Document Type
Article
DOI
https://doi.org/10.14358/PERS.79.6.545
Publication Date
2013
Recommended Citation
Miao, Xin, Xiaomin Qiu, Shuo-Sheng Wu, Jun Luo, Douglas R. Gouzie, and Hongjie Xie. "Developing efficient procedures for automated sinkhole extraction from lidar DEMs." Photogrammetric Engineering & Remote Sensing 79, no. 6 (2013): 545-554.
Journal Title
Photogrammetric Engineering & Remote Sensing
