Developing Efficient Procedures for Automated Sinkhole Extraction from Lidar DEMs

Citation

Material Information

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

Subjects

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

Notes

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

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.

USFLDC Membership

Aggregations:
University of South Florida
Karst Information Portal

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serial

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