Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs


Material Information

Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs
Series Title:
Photogrammetric Engineering & Remote Sensing
Miao, Xin
Qiu, Xiaomin
Wu, Shuo-Sheng
Luo, Jun
Gouzie, Douglas R.
Xie, Hongjie
Ingenta Connect
Publication Date:


Subjects / Keywords:
Sinkhole Detection ( local )
Karst Areas ( local )
Adaptive Wiener Filter (AWF) ( local )
Hierarchal Watershed Segmentation (HWS) ( local )
serial ( sobekcm )


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.
Original Version:
Photogrammetric Engineering & Remote Sensing, Vol. 6 (2013-06-01).

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